BEGIN:VCALENDAR VERSION:2.0 PRODID:-//sebbo.net//ical-generator//EN NAME:ApacheCon 2020 X-WR-CALNAME:ApacheCon 2020 BEGIN:VEVENT UID:acah2020-bigdata-1-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Apache Hadoop YARN: Past\, Now and Future LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSzilard Nemeth\, Sunil Govindan\n\n

\nApache Hadoop YARN is an integral part of on-premiss solutio ns and it will be for the foreseeable future. We also believe\, it will ha ve an important role in the Cloud as well allowing users to move their sol utions both to private and public cloud as easily as possible. For this re ason\, the development of YARN took a new momentum in the last year. In th is talk\, we will talk about the latest updates from the community which w ill be released in Hadoop 3.3.x/3.4.x releases: For compute acceleration d evices (including GPU/FPGA\, etc.)\, we will talk about better GPU support (including GPU hierarchy scheduling support\, Nvidia-docker v2 support\, etc.)\; YARN’s device plugin framework to allow developers easier add new compute-acceleration devices\; FPGA support\, etc. For scheduling related improvements\, we will talk about new improvements of global scheduling in CapacityScheduler\, new efforts to bring dynamic-queue-creation/absolute- resource into production\, and one of the recent work\, fs2cs\, which allo ws user migrate from FairScheduler to CapacityScheduler. Apart from these\ , we will talk about new containerization improvements: runc support\, imp rovements of log aggregation to better support cloud storage\, etc. Audien ces will get the latest development progress of Apache Hadoop YARN\, and h elp them to make a decision when upgrading to Hadoop 3.x.\n

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\nSzilard Nemeth:
\nSzilard Nemeth has many years of development expe rience mainly in Java. He joined Cloudera’s YARN team late 2017 and has be en key to transfer YARN knowledge from Palo Alto to Budapest\, Hungary. He has become a Hadoop committer in the Summer of 2019. Through out his care er in YARN he mostly focused on Custom Resource Types\, GPU support and re cently he has been involved in making YARN more flexible and ready to the Cloud. He lives in Budapest but when has the time\, he loves traveling.
\nSunil Govindan:
\nEngineer Manager @Cloudera. Contributing to Ap ache Hadoop project since 2013 in various roles as Hadoop Contributor\, Ha doop Committer\, and a member Project Management Committee (PMC). Majorly working on YARN Scheduling improvements / Multiple Resource types support in YARN etc. He also served as Apache Submarine PMC member\, Apache YuniKo rn (incubating) PMC member.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Hadoop Storage Reloaded: the 5 lessons Ozone learned from HDFS LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMárton Elek\n\n

\nApache (Hadoop) Ozone is a brand-new storage system for the Hadoop ecosystem. It provides Object Store semantics (like S3) and can handle billions of obje cts. Ozone doesn't depend on HDFS but it's the \"spiritual successor\" of it. The lessons learned during the 10+ years of HDFS helped to design a mo re scalable object store. This presentation explains the key challenges of a storage system and shows how the specific problems can be answered.\n\n\n

\nMarton Elek is PMC in Apache Hadoop and Apache Ratis projec ts and working on the Apache Hadoop Ozone at Cloudera. Ozone is a new Hado op sub-project which provides an S3 compatible Object Store for Hadoop on top of a new generalized binary storage layer. He is also working on the c ontainerization of Hadoop and creating different solutions to run Apache B ig Data projects in Kubernetes and other could native environments.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-T1735@apachecon.com SEQUENCE:2 DTSTAMP:20200813T193041Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Building efficient and reliable data lakes with Apache Iceberg LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAnton Okolnychyi\, Vishwanath Lakku ndi\n\n

\nApache Iceberg is a table format that allows data eng ineers and data scientists to build reliable and efficient data lakes with features that are normally present only in data warehouses. This talk wil l be a deep dive into key design principles of Apache Iceberg that enable the following features on top of data lakes: - ACID compliance on top of a ny object store or distributed file system - Flexible indexing capabilitie s which boost the performance of highly selective queries - Implicit parti tioning using partition transforms - Reliable schema evolution - Time trav el and rollback These advanced features let companies substantially simpli fy their current architectures as well as enable new use cases on top of d ata lakes.\n

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\nAnton is a committer and PMC member of Apache Iceberg as well as an Apache Spark contributor at Apple. At Apple\, he is working on making data lakes efficient and reliable. Prior to joining App le\, he optimized and extended a proprietary Spark distribution at SAP. An ton holds a Master’s degree in Computer Science from RWTH Aachen Universit y.\n
\nVishwanath Lakkundi is the engineering lead for the team that\ nfocuses on Data Orchestration and Data Lake at Apple. This team is\nrespo nsible for development of an elastic fully managed Apache Spark as\na serv ice\, a Data Lake engine based on Apache Iceberg and a data\npipelines pro duct based on Apache Airflow. He has been working with\nApple since the la st seven years focusing on various analytics\ninfrastructure and platform products.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Accelerating distributed joins in Apache Hive: Runtime filtering e nhancements LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPanagiotis Garefalakis\, Stamatis Z ampetakis\n\n

\nApache Hive is an open-source relational databa se system that is widely adopted by several organizations for big data ana lytic workloads. It combines traditional MPP (massively parallel processin g) techniques with more recent cloud computing concepts to achieve the inc reased scalability and high performance needed by modern data intensive ap plications. Even though it was originally tailored towards long running da ta warehousing queries\, its architecture recently changed with the introd uction of LLAP (Live Long and Process) layer. Instead of regular container s\, LLAP utilizes long-running executors to exploit data sharing and cachi ng possibilities within and across queries. Executors eliminate unnecessar y disk IO overhead and thus reduce the latency of interactive BI (business intelligence) queries by orders of magnitude. However\, as container star tup cost and IO overhead is now minimized\, the need to effectively utiliz e memory and CPU resources across long-running executors in the cluster is becoming increasingly essential. For instance\, in a variety of productio n workloads\, we noticed that the memory bandwidth of early decoding all t able columns for every row\, even when this row is dropped later on\, is s tarting to overwhelm the performance of single query execution. In this ta lk\, we focus on some of the optimizations we introduced in Hive 4.0 to in crease CPU efficiency and save memory allocations. In particular\, we desc ribe the lazy decoding (or row-level filtering) and composite bloom-filter s optimizations that greatly improve the performance of queries containing broadcast joins\, reducing their runtime by up to 50%. Over several produ ction and synthetic workloads\, we show the benefit of the newly introduce d optimizations as part of Cloudera’s cloud-native Data Warehouse engine. At the same time\, the community can directly benefit from the presented f eatures as are they 100% open-source!\n

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\nPanagiotis Garefal akis:
\nPanagiotis Garefalakis is a Software Engineer at Cloudera whe re he is part of the Data Warehousing team. He holds a Ph.D. in Computer S cience from Imperial College London were he was affiliated with the Large- Scale Data & Systems (LSDS) group. His interests lie within the broad area of systems including large-scale distributed systems\, cluster resource m anagement\, and big data processing.
\nStamatis Zampetakis:
\nSt amatis Zampetakis is a Software Engineer at Cloudera working on the Data W arehousing product. He holds a PhD in Big Data management on massively par allel systems\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Big Data File Format Cost Efficiency - Millions of Dollars Deal LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nXinli Shang\, Juncheng Ma\n\n

\nReducing the size of data at rest and in transit is critical to ma ny organizations\, not only because it can save the cost of storage but al so can improve the IO usage and traffic volume in the network. We will pre sent how to translate hundreds of petabytes data compressed in GZIP in the data lake to a more efficient compression method - ZSTD which can reduce the data size by 10% and save millions of dollars. We will show the recent Apache Parquet format improvement that makes ZSTD compression to be easil y set up (PARQUET-1866). The tech talk will also demonstrate how we solve the challenges of the compression translation speed by improving the throu ghput by 5X (PARQUET-1872). As a result\, the translation time of large sc ale data sets can be reduced from months to days and save compute vCores c orrespondingly. The translation needs to be in a safe way to prevent data corruption and incompatibility. We will also show the technicals that are built into the compression translation tool to prevent them from happening . Another significant storage size reduction (up to 80%) can be done by 1) reordering the columns in Parquet to make it more friendly to encoding an d compression\, 2) encoding with BYTE_STREAM_SPLIT (PARQUET-1622) that is more efficient for floating type data\, 3) reducing geolocation data preci sion to make RLE more efficient\, 4) pruning unused columns (PARQUET-1800) . We will show above every technique\, the effectiveness of each and the r eason behind it. We will also show the tools like Parquet column-size (PAR QUET-1821) that can help users to identify the candidate tables to apply t he above techniques.\n

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\nXinli Shang:
\nXinli Shang is a tech lead on the Uber Data Infra team\, Apache Parquet Committer. He is passionate about big data file format for efficiency\, performance and sec urity\, tuning large scale services for performance\, throughput\, and rel iability. He is an active contributor to Apache Parquet. He also has many years of experience developing large scale distributed systems like S3 Ind ex\, and operating system Windows.
\nJuncheng Ma :
\nSoftware De veloper at Uber\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R0900@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:Integrate Apache Flink with Cloud Native Ecosystem LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nYang WangTao Yang\n\n

\n With the vigorous development of cloud-native and serverless computing\, t here are more and more big-data workloads\, especially Apache Flink worklo ads\, running in Alibaba cloud for better deployment and management\, back ed by Kubernetes. This presentation introduces the experiences of intergra ting Flink with cloud-native ecosystem\, including the improvements in Fli nk to support elasticity and natively running on Kubernetes\, the experien ces about managing dependent components like ZooKeeper\, HDFS etc. and lev eraging Kubernetes service/network/storage extensions\, better supports fo r big-data workloads to satisfy requirements on multi-tanent management\, resource fairness\, resource elasticity etc. and achieve high scheduling p erformance via Apache YuniKorn.\n

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\nYang Wang:
\nTechni cal Expert of Alibaba's realtime computing team\, Apache Flink Contributor \, focusing on the direction of resource scheduling in Flink.
\nTao Y ang:
\nTechnical Expert of Alibaba's realtime computing team\, Apache Hadoop Committer\, Apache YuniKorn Committer\, focusing on the direction of resource scheduling in YARN and Kubernetes.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R0900 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Next Gen Data Lakes using Apache Hudi LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBalaji Varadarajan\, Sivabalan Nara yanan\n\n

\nData Lakes are one of the fastest growing trends in managing big data across various industries. Data Lakes offer massively s calable data processing over vast amounts of data. One of the main challen ges that companies face in building a data lake is designing the right pri mitives for organizing their data. Apache Hudi helps implement uniform\, b est-of-breed data lake standards and primitives. With such primitives in p lace\, next generation data lake would be about efficiency and intelligenc e. Businesses expect their data lake installations to cater to their ever changing needs while being cost efficient. In this talk\, we will discuss new features in Apache Hudi that is catered towards building next-gen data -lake. We will start with basic Apache Hudi primitives such as upsert & de lete required to achieve acceptable latencies in ingestion while at the sa me time providing high quality data by enforcing schematization on dataset s. We will look into the novel “record level index” supported by Apache Hu di and how it supports efficient upserts. We will then dive into how Apach e Hudi supports query optimization by leveraging its rich metadata. Effici ent storage management is a key requirement for large data lake installati on. We will look at how Apache Hudi supports intelligent and dynamic re-cl ustering of data for better storage management and faster query times. Fin ally\, we will discuss how to easily onboard your existing dataset to Apac he Hudi format\, so you can leverage Apache Hudi efficiency without making any drastic changes to your existing data lake.\n

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\nBalaji Varadarajan:
\nBalaji Varadarajan is currently a Staff Engineer in Ro binhood's data platform team. Previously he was a tech lead in Uber data p latform working on Apache Hudi and Hadoop platform at large. Previously\, he was one of the lead engineers in LinkedIn’s databus change capture syst em. Balaji’s interests lie in large-scale distributed data systems.
\ nSivabalan Narayanan:
\nSivabalan Narayanan is a senior software engi neer at Uber overseeing data engineering broadly across the network perfor mance monitoring domain. He is an active contributor to Apache Hudi and al so big data enthusiasist whose interest lies in building data lake technol ogies. Previously\, he was one of the core engineers responsible for buili ding Linkedin's blob store.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:A Production Quality Sketching Library for the Analysis of Big Dat a LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nLee Rhodes\n\n

\nIn the analysis of big data there are often problem queries that don’t scale beca use they require huge compute resources to generate exact results\, or don ’t parallelize well. Examples include count-distinct\, quantiles\, most fr equent items\, joins\, matrix computations\, and graph analysis. Algorithm s that can produce accuracy guaranteed approximate answers for these probl em queries are a required toolkit for modern analysis systems that need to process massive amounts of data quickly. For interactive queries there ma y not be other viable alternatives\, and in the case of real­-time streams \, these specialized algorithms\, called stochastic\, streaming\, sublinea r algorithms\, or 'sketches'\, are the only known solution. This technolog y has helped Yahoo successfully reduce data processing times from days to hours or minutes on a number of its internal platforms and has enabled sub second queries on real-time platforms that would have been infeasible with out sketches. This talk provides an introduction to sketching and to Apach e DataSketches\, an open source library in C++\, Java and Python of algori thms designed for large production analysis systems.\n

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\nLee Rhodes is a Distinguished Architect at Yahoo (now Verizon Media). He crea ted the DataSketches project in 2012 to address analysis problems in Yahoo 's large data processing pipelines. DataSketches was Open Sourced in 2015 and is in incubation at Apache Software Foundation. He was an author or co author on sketching work published in ICDT\, IMC\, and JCGS. He obtained h is Master's Degree in Electrical Engineering from Stanford University.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Cylon - Fast Scalable Data Engineering LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSupun Kamburugamuve\, Niranda Perer a\n\n

\nMachine learning (ML) and deep learning (DL) fields hav e made amazing progress in the past few years. Modern ML/DL applications h ave outgrown resource requirements beyond a single node's capability. Howe ver\, this is just a small part of the issues facing the overall data proc essing environment\, which must also support a raft of big data engineerin g for pre- and post-data processing\, communication\, and system integrati on. The big data tools surrounding the ML/DL applications need to be able to easily integrate with existing ML/DL frameworks in a multitude of langu ages\, which particularly increases user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for d ata processing\, yet many of today's popular data analytics tools are unab le to satisfy all these requirements at the same time. This presentation i ntroduces Cylon\, an open-source high performance distributed data process ing library that can be seamlessly integrated with the existing Big Data a nd AI/ML frameworks. It is developed with a flexible C++ core on top of th e Apache Arrow data format and exposes language bindings to C++\, Java\, a nd Python. The presentation discusses Cylon's architecture in detail and r eveals how it can be imported as a library to existing applications or ope rate as a standalone framework. Initial experiments show that Cylon outper forms popular tools such as Apache Spark and Dask with major performance i mprovements for key operations with the ability to integrate with them. Fi nally\, we show how Cylon can enable high-performance data pre-processing in popular AI tools such as Pytorch\, Tensorflow\, and Jupyter notebook wi thout taking away Data scientists’ productivity.\n

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\nSupun K amburugamuve:
\nSupun Kamburugamuve has a Ph.D. in computer science s pecializing in high-performance data analytics. He is working in the role of a principal software engineer at the Digital Science Center of Indiana University where he leads Twister2 and Cylon high-performance data analyti cs projects. Supun is an elected member of the Apache Software Foundation and has contributed to many open-source projects including Apache Web Serv ices projects and Apache Heron. Before joining Indiana University\, Supun worked on middleware systems and was a key member of the WSO2 ESB project\ , which is an open-source enterprise integration solution being widely use d by enterprises. Supun has given many technical talks at research confere nces and technical conferences including Strata NY\, Big Data Conference\, and Apache Con.
\nNiranda Perera:
\nNiranda Perera is a second- year grad student at Indiana University - Luddy School of Informatics\, Co mputing\, and Engineering (SICE). He is enrolled in the Intelligent System s Engineering Department and advised by Prof. Geoffery Fox. He is an activ e contributor to the Twister2 project and a founding member of the Cylon h igh-performance data analytics project. He completed his Bachelor's at the University of Moratuwa\, Sri Lanka\, and prior to joining IU\, he was a c ontributor to WSO2 Data Analytics Server\, which was a part of WSO2 middle ware stack.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Snakes on a Plane: Interactive Data Exploration with PyFlink and Z eppelin Notebooks LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMarta Paes\n\n

\nStream processing has fundamentally changed the way we build and think about data pipelines — but the technologies that unlock the value of this powerful p aradigm haven’t always been friendly to non-Java/Scala developers. Apache Flink has recently introduced PyFlink\, allowing developers to tap into st reaming data in real-time with the flexibility of Python and its wide ecos ystem for data analytics and Machine Learning. In this talk\, we will expl ore the basics of PyFlink and showcase how developers can make use of a si mple tool like interactive notebooks (Apache Zeppelin) to unleash the full power of an advanced stream processor like Flink.\n

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\nMarta is a Developer Advocate at Ververica (formerly data Artisans) and a contr ibutor to Apache Flink. After finding her mojo in open source\, she is com mitted to making sense of Data Engineering through the eyes of those using its by-products. Marta holds a Master’s in Biomedical Engineering\, where she developed a particular taste for multi-dimensional data visualization \, and previously worked as a Data Warehouse Engineer at Zalando and Accen ture.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Unified Data Processing with Apache Spark and Apache Pulsar LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJia Zhai\n\n

\nLambda is widely used in the industry when people need to process both real-time an d historical data to get a result. It is effective\, and a good balance of speed and reliability. But there are still challenges to use Lambda in th e practice. The biggest detraction has been the need to maintain two disti nct (and possibly complex) systems to generate both batch and streaming la yers. Thus\, the operational cost of maintaining multiple clusters is nont rivial\, and in some cases\, one business logic would have to be split int o many segments across different places\, which is a challenge to maintain as the business grows and it also increases communication overhead. In th is session\, we'd like to present a unique data processing architecture wi th Apache Spark and Apache Pulsar\, a solution\, with the core idea of \"O ne data storage\, one computing engine\, and one API\"\, to solve the prob lems of Lambda architecture.\n

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\nJia Zhai is the co-founder of StreamNative\, as well as PMC member of both Apache Pulsar and Apache B ookKeeper\, and contributes to these two projects continually.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-W1935@apachecon.com SEQUENCE:1 DTSTAMP:20200813T193041Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Cluster Management in Apache Ecosystem & Kubernetes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShekhar Prasad Rajak\n\n

\nApache have powerful cluster & resource manager already\, so do we real ly need to use Kubernetes for the deployment while using Apache projects ? Let's find out what type of cluster management system Apache already have \,How cluster management works in each of below cases and when we don't n eed any other cluster management top of it and when we can leverage the po wer of both this apache cluster modes and Kubernetes in resource & cluster management. * Apache Spark Standalone: A simple cluster manager available as part of the Spark distribution.. * Apache Mesos: A general purpose dis tributed OS level push based scheduler & resource manager. * Apache Hadoop YARN: A distributed computing framework for monolithic job scheduling and cluster resource management for Hadoop cluster (Apache/CDH/HDP) We will s ee some benchmarks and features that kubernetes can provide but it is not present(or not mature enough) in the Apache ecosystem\, but still using\, one or both can improve the performance. We will deep dive into fundamenta ls of Kubernetes and Apache distribution\, resource & cluster management s ystem\, Job scheduling\, to get clear cut idea behind both ecosystems and why they are best in particular cases like Big Data\, Machine Learning\, L oad balancer\, and so on. Applications are containerised in Kubernetes Pod \, Kubernetes Service is used as Load balancer\, Kubernetes High availabil ity is because of distribution of Pods in worker nodes\, Local Storage\, P ersistent volume & Networking and many other features will be compared sid e by side with Apache Ecosystem. Like in Mesos\, Application Group models dependencies as a tree of groups and Components are started in dependency order\, Mesos-DNS works as basic load balancer\, applications distribution among slave nodes\, two-level scheduling mechanism\, modern kernel \"cgro ups\" in Linux & \"zones\" in Solaris\, and so on. Along with the comparis on & benchmark the talk will provide practical guide to use the Apache pro ject with Kubernetes. Audience will understand the Software System design and generic problems of processing the request through the cluster & resou rce managers and why it is important to have modular\, micro service based \, loosely coupled software design\, so that it can easily go through the container or OS level cluster management systems. This talk is clearly not to show who is winning but how can you win in your time\, in the dark sit uation.\n

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\nShekhar is passionate about Open Source Software s and active in various Open Source Projects. During college days he has c ontributed SymPy - Python library for symbolic mathematics \, Data Science related Ruby gems like: daru\, dart-view(Author)\, nyaplot - which is und er Ruby Science Foundation (SciRuby)\, Bundler: a gem to bundle gems\, Num Py & SciPy for creating the interactive website and documentation website using sphinx and Hugo framework\, CloudCV for migrating the Angular JS app lication to Angular 8\, and few others. He has successfully completed Goog le Summer of Code 2016 & 2017 and mentored students after that on 2018\, 2 019. Shekhar also talked about daru-view gem in RubyConf India 2018 and Py Con India 2017 on SymPy & SymEngine.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R1615@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Column encryption & Data Masking in Parquet - Protecting data at t he lowest layer LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPavi Subenderan\, Xinli Shang\n\n

\nIn a typical big dataset\, only a minority of columns are actu ally sensitive and need to be protected. Columnar file formats like Apache Parquet allow for column level access control through encryption. This me ans the small number of sensitive columns in a dataset can be protected th rough encryption\, while the non-sensitive columns can be open for access. Data masking features for encrypted columns bring further convenience and allows users to leverage encrypted columns even without access to them. T he combination of column encryption and data masking maximizes accessibili ty to your data without compromising the security of sensitive data. In th e first half\, we will go over column encryption design and features in Pa rquet. We will cover considerations when operating parquet column encrypti on in production like Key Management Service\, performance tradeoffs\, enc ryption algorithm choice\, etc. In the second half\, we will cover the new Data Masking features in Parquet. There will be discussion about motivati on behind data masking\, the security implications of masking and implemen tation. Finally we will look at the tradeoffs between the different types of masks and the limitations of each type in terms of compression ratio\, table joins\, usability and administration overhead.\n

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\nPav i Subenderan:
\nPavi is a Software Engineer on Uber's Data Infra team . His focus is on data security\, privacy and open source big data technol ogies. He has been working on Parquet column encryption for 1.5 years and more recently on data masking.
\nXinli Shang:
\nXinli Shang is a tech lead on the Uber Data Infra team\, Apache Parquet Committer. He is p assionate about big data file format for efficiency\, performance and secu rity\, tuning large scale services for performance\, throughput\, and reli ability. He is an active contributor to Apache Parquet. He also has many y ears of experience developing large scale distributed systems like S3 Inde x\, and operating system Windows.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R1655@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:GDPR’s Right to be Forgotten in Apache Hadoop Ozone LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDinesh Chitlangia\n\n

\n Apache Hadoop Ozone is a robust\, distributed key-value object store for H adoop with layered architecture and strong consistency. It isolates the na mespace management from the block and node management layer\, which allows users to independently scale on both axes. Ozone is interoperable with th e Hadoop ecosystem as it provides OzoneFS (Hadoop compatible file system A PI)\, data locality and plug-n-play deployment with HDFS as it can be inst alled in an existing Hadoop cluster and can share storage disks with HDFS. Ozone solves the scalability challenges with HDFS by being size agnostic. Consequently\, it allows users to store trillions of files in Ozone and a ccess them as if they are on HDFS. Ozone plugs into existing Hadoop deploy ments seamlessly\, and programs like Yarn\, MapReduce\, Spark\, Hive and w ork without any modifications. In the era of increasing need for data priv acy and regulations\, Ozone also provides built-in support for GDPR compli ance with strong focus on Right to be Forgotten i.e.\, Data Erasure. At th e end of this presentation the audience will be able to understand: 1. HDF S scalability challenges 2. Ozone’s Architecture as a solution 3. Overview of GDPR 4. GDPR implementation in Ozone\n

\n\n

\nDinesh is a Sof tware Engineer with strong expertise in Java\, Distributed Systems for ~10 years. He has been involved with Hadoop ecosystem for the last 4 years an d is an Apache Hadoop Committer. Dinesh is currently working at Cloudera\, performing the role of a proactive support consultant for Premier custome rs and enjoys contributing to Open Source community. Outside of technology \, Dinesh has a serious hobby in Landscape/Cityscape photography.\n CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R1735@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Global File System View Across all Hadoop Compatible File Systems with the LightWeight Client Side Mount Points. LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nUma Maheswara Rao Gangumalla\n\n

\nApache Hadoop File System layer has integrations to many popula r storage systems including cloud storages like S3\, Azure Data Lake Stora ge etc\, along with in-house Apache Hadoop Distributed File System. When u sers want to migrate data between file systems\, it’s very difficult for t hem to update their meta storages when they persist file system paths with schemes. For Example the Apache Hive persists the URI paths in meta-store . In Apache Hadoop\, we came up with a solution(HDFS-15289) for this probl em\, i.e\, the View FileSystem Overload Scheme with the configurable schem e and mount points. In this talk\, we will cover in details\, how users ca n enable it and how easily users can migrate data between file systems wit hout modifying their meta-store. It’s completely transparent to users with respective to the file paths. We will present one of the use cases with A pache Hive partitioning\, that is the user can move one/some of their part ition data to a remote file system and just add a mount point on the defau lt file system(ex: HDFS) where the user was working with. Here Hive querie s will work transparently from the user point of view even though the data resides in a remote storage cluster ex: Apache Hadoop Ozone or S3. This w ill be very useful when users want to move certain kinds of data\, ex: Col d Partitions\, Small Files can be moved to remote clusters from a primary HDFS cluster without affecting applications. The Mount tables are maintain ed at the central server\, all clients will load the tables while initiali zing the file system and also can refresh on modification of mount points. So\, that all the initializing clients will be in sync. This will make us er’s life easier to migrate data between cloud and on-premise storages in a much flexible way.\n

\n\n

\nUma Maheswara Rao Gangumalla is an Apache Software Foundation Member[1]. An Apache Hadoop\, BookKeeper\, Incu bator committer and a member of the Project Management Committee[2]\, and a long-term active contributor to the Apache Hadoop project. He is also me ntoring several incubator projects at Apache. Uma holds a bachelor's degre e in Electronics and Communications Engineering. He has more than 13 years of experience in large scale Distributed Software Platforms design and de velopment. Currently\, Uma is working as a Principal Software Engineer at Cloudera\, Inc\, California\, and primarily focuses on open source big dat a technologies. Prior to this\, Uma worked as a Software Architect in Inte l Corporation\, California. [1] https://www.apache.org/foundation/members. html [2] http://people.apache.org/phonebook.html?uid=umamahesh\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R1815@apachecon.com SEQUENCE:2 DTSTAMP:20200828T190237Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Apache Hadoop YARN fs2cs: Converting Fair Scheduler to Capacity Sc heduler LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBenjamin Teke\n\n

\nApac he Hadoop YARN has two popular schedulers\, Fair Scheduler and Capacity Sc heduler. Although the two are based on different principles\, convergent e volution pushed them to be similar both in functionality and the feature s et they offer. By now it seems to be a good idea to merge the two or chose one over the other so the entire user base can enjoy one unified support effort and knowledge base. In this talk\, we will present our approach whi ch is offering users a way to migrate from Fair Scheduler to Capacity Sche duler by exploring migration paths and filling feature parity gaps. We wil l also talk about challenges and those aspects of the migration need some engineering effort in order to keep the achievements of fine-tuning Fair S cheduler installations over many years. We will explain why Capacity Sched uler is our scheduler of choice\, the way we analyzed differences between the two schedulers\, how we found migration paths\, and finally\, we will present a tool (fs2cs) we developed to help users automate the process.\n< /p>\n\n

\nBenjamin is a senior software developer with many years of experience in the presentation of bigdata for the telecom industry (mainl y Kafka and HBase). Since early 2020\, he has been an integral part of the YARN team at Cloudera. He gained general knowledge in YARN\, and recently he started to specialise in Schedulers. He lives in Budapest and besides his interest in photography and cars\, he is passionately automatizing his home via IoT.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R1855@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:HDFS Migration from 2.7 to 3.3 and enabling Router Based Federatio n (RBF) in production LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAkira Ajisaka\n\n

\nIn a production HDFS cluster in Yahoo! JAPAN\, the namespace has become too la rge and it won't fit in a single NameNode in the near future. Therefore we want to split the big namespace into some small one and use federation. T here is an existing ViewFS solution but the clients need to add the mount table in their configs when using the ViewFS. Our clusters have too many c lients\, so we want to minimize the change of client configs. RBF is a new solution. In contrast to ViewFS\, the Router manages the mount table\, an d clients don't have to set the mount table explicitly. In this talk\, I w ill introduce the internals of RBF and how we configured the mount tables to load-balance among namespaces in production. RBF has some limitations. For example\, rename (mv) operation is not allowed between different names paces. I will talk about how we work around the limitations. In addition\, the developers are going to eliminate some of the limitations in the comm unity. I'll introduce the progressions as well. Next\, I will introduce th e improvements of recent HDFS. For example\, multiple standby NameNodes\, observer NameNodes\, and DataNode maintenance mode are features that will greatly reduce the operation cost. I'm going to introduce them and how to enable those features. Upgrading from 2.7 to 3.3 is a big jump and we hit many incompatible changes. For the administrators who are going to upgrade their HDFS clusters\, I would like to introduce the differences as many a s possible.\n

\n\n

\nAkira Ajisaka develops and validates some ne w features of Apache Hadoop such as HDFS Router Based Federation for our u se. Also\, he troubleshoots and improves management/operations in our Hado op clusters. He maintenances Apache Hadoop to improve its quality as an Ap ache Hadoop/Yetus committer and PMC member.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-1-R1935@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20201001T193500Z DTEND:20201001T201500Z SUMMARY:Apache Beam: using cross-language pipeline to execute Python code from Java SDK LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAlexey Romanenko\n\n

\nT here are many reasons why we would need to execute Python code in Java dat a processing pipelines (and vice versa) - e.g. Machine Learning libraries\ , IO connectors\, user’s Python code - and several different ways to do th at. With the End of Life of Python 2 started this year\, it’s getting more challenging since not all old solutions still work well for Python 3. One of the potential options for this could be using a cross-language pipelin e and Portable Runner in Apache Beam. In this talk I’m going to talk about what the cross-language pipeline in Beam is\, how to create a mixed Java/ Python pipeline\, how to set up and run it\, what kind of requirements and pitfalls we can expect in this case. Also\, I’ll show a demo of a use cas e where we need to execute a custom user’s Python 3 code in the middle of Java SDK pipeline and run it with Portable Spark Runner.\n

\n\n

\ nAlexey Romanenko is Principal Software Engineer at Talend France\, with m ore than 18 years of experience in software development. During his career \, he has been working on very different projects\, like high-load web ser vices\, web search engine and cloud storages. He is Apache Beam PMC member and committer\, he contributed to different Beam IO components and Spark Runner.\n

CATEGORIES:Big Data URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-1.html#R1935 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Spark and Iceberg at Apple's Scale - Leveraging differential files for efficient upserts and deletes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAnton Okolnychyi\, Vishwanath Lakku ndi\n\n

\nApple leverages Apache Spark for processing large dat asets to power key components of Apple’s production services. As users beg in to use Apache Spark in a bigger range of data processing scenarios\, it is essential to support efficient and transactional update/delete/merge o perations even in read-mostly data lake environments. For example\, such f unctionality is required to implement change data capture\, support some f orms of slowly changing dimensions in data warehousing\, fix corrupted rec ords without rewriting complete partitions. The original implementation of update/delete/merge operations in Apple's internal version of Apache Spar k relied on snapshot isolation in Apache Iceberg and rewriting complete fi les if at least one record had to be changed. This approach performs well if we can limit the scope of updates/deletes to a small number of files us ing indexing. However\, modifying a couple of records in a large number of files is still expensive as all unmodified records in touched files have to be copied over. Therefore\, Apple collaborates with other members of th e Apache Iceberg and Apache Spark communities on a way to leverage differe ntial files\, an efficient method for storing large and volatile databases \, for update/delete/merge operations. This approach allows us to reduce w rite amplification\, support online updates to data warehouses and sustain more concurrent operations on the same table. This talk will briefly desc ribe common ways to implement updates in analytical databases\, challenges between providing updates and optimizing data structures for reading\, ou tline the proposed solution alongside its benefits and drawbacks.\n

\n\ n

\nAnton is a committer and PMC member of Apache Iceberg as well as an Apache Spark contributor at Apple. He has been dealing with internals of various Big Data systems for the last 5 years. At Apple\, Anton is work ing on data lakes and an elastic\, on-demand\, secure\, and fully managed Spark as a service. Prior to joining Apple\, he optimized and extended a p roprietary Spark distribution at SAP. Anton holds a Master’s degree in Com puter Science from RWTH Aachen University.\n
\nVishwanath Lakkundi is the engineering lead for the team that\nfocuses on Data Orchestration and Data Lake at Apple. This team is\nresponsible for development of an elast ic fully managed Apache Spark as\na service\, a Data Lake engine based on Apache Iceberg and a data\npipelines product based on Apache Airflow. He h as been working with\nApple since the lastseven years focusing on various analytics\ninfrastructure and platform products.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-T1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Presentation of DLab toolset LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMykola Bodnar\, Vira Vitanska\, Ole g Fuks\n\n

\nWe are going to introduce DLab: - Similar user exp erience across AWS\, GCP\, and Azure clouds - Automatically configurable e xploratory environment integrated with enterprise security and templates - Project level collaboration environment across multiple clouds - Aggregat ed billing with cost comparison across cloud providers - Project level res ource management \n

\n\n

\nMykola Bodnar:
\nMykola Bodnar ha s worked in EPAM since 2019\, primary skill DevOps.BigData\;
\nVira V itanska:
\nVira has worked in EPAM since 2017 as a functional tester.
\nOleg Fuks
\nOleg Fuks has been working in EPAM since 2019\, p rimary skill is Java.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-T1735@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Efficient Spark Scheduling on K8s with Apache YuniKorn LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nWeiwei Yang\, Gao Li\n\n

\nApache Yunicorn (Incubating) is a new open-source project\, which is a standalone resource scheduler of container orchestration platforms. Curren tly\, it provides a fully functional resource scheduler alternative for K8 s that manages and schedules Big Data workloads. We embrace Apache Spark f or data engineering and machine learning\, and by running Spark on K8s\, w e are able to exploit compute power promisingly under such highly elastic\ , scalable\, and multi-paradigm architecture. We made a lot of effort on e nhancing the core resource scheduling\, in order to bring high performance \, efficient-sharing\, and multi-tenancy oriented capabilities to Spark jo bs. In this talk\, we will focus on revealing the architecture of the clou d-native infrastructure\; How we leverage YuniKorn scheduler to redefine t he resource scheduling on Cloud. We will introduce how YuniKorn manages qu otas\, resource sharing\, and auto-scaling\, and ultimately how to schedul e large scale Spark jobs efficiently on Kubernetes in the cloud.\n

\n\n

\nWeiwei Yang:
\nWeiwei Yang is a Staff Software Engineer from Cloudera\, an Apache Hadoop committer and PMC member. He is focused on te chnology around large scale\, hybrid computation systems. Before Cloudera\ , he worked in Alibaba’s realtime computation infrastructure team that ser ves large scale big data workloads. Currently\, Weiwei is leading the effo rts for resource scheduling and management on K8s. Weiwei holds a master’s degree from Peking University.
\nGao Li:
\nLi Gao is an enginee ring lead and infrastructure software engineer from Databricks\, a leading open source and cloud vendor focusing on unified analytics cloud. Li’s fo cuses are mainly on large scale distributed infrastructure across public c loud vendors and bringing kubernetes to the AI and big data compute worklo ads. Prior to Databricks\, Li has led major data infrastructure and data p latform efforts in companies such as Lyft\, Fitbit\, Salesforce\, etc.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-T1815@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Everything you want to know about running Apache Big Data projects on ARM datacenters LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nZhenyu Zheng\, Sheng Liu\n \n

\nWith more and more vendors starts to provide ARM based chips\, PCs and datacenters\, more and more people starts to think about the portabili ty of Apache big data projects running on ARM based hardwares: Will it run ? How will it perform? Are there any difference running on different platf orms? In this session\, we will introduce what we have done in the past ye ars to make Apache Big Data projects be able to running on ARM platforms\, including the problems we sloved\, how did we add CIs to related projects to provide a long-term ARM support\, and what are the remaining gaps in p rojects like Hadoop\, Spark\, Hive\, HBase\, Kudu etc.\, these general ide as can provide very useful information for users\, devlopers and other pro jects that are intrested to verify thier portability on ARM platforms. We will also introduce some of our comparison results between running Big Dat a cluster on ARM datacenter and x86 datacenters\, together with some impro vement ideas which could make Big Data projects perform better on ARM data centers.\n

\n\n

\nZhenyu Zheng:
\nWorking in Open Source com munities for over 5 years\, currently focus on ARM portability for Open So urce software.
\nSheng Liu:
\nWorking on OpenSource for over 7 y ears\, currently focus on arm portability for big data projects and perfor mance tuning\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Heating Up Analytical Workloads with Apache Druid LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGian Merlino\n\n

\nApach e Druid is a modern analytical database that implements a memory-mappable storage format\, indexes\, compression\, late tuple materialization\, and a vectorized query engine that can operate directly on compressed data. Th is talk goes into detail on how Druid's query processing layer works\, and how each component contributes to achieving top performance for analytica l queries. We'll also share war stories and lessons learned from optimizin g Druid for different kinds of use cases.\n

\n\n

\nGian Merlino i s CTO and a co-founder of Imply\, a San Francisco based technology company \, and a committer on Apache Druid. Previously\, Gian led the data ingesti on team at Metamarkets (now a part of Snapchat) and held senior engineerin g positions at Yahoo. He holds a BS in Computer Science from Caltech.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-T1935@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:FOSS Never Forgets: An Introduction to Free and Open Source Soluti ons for Data Processing and Management with an Emphasis on Fault Tolerance LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKatie McMillan\n\n

\nThi s talk will provide an introduction to data processing and management in t wo of the most popular Free and Open Source Solutions: Hadoop and PostgreS QL. The talk will link these solutions to current challenges in big data\, with an emphasis on the fault tolerancy of each solution. It will then sh ow how the solutions can be used together\, using Foreign Data Wrappers. B y combining the functionality of PostgreSQL and Hadoop with Foreign Data W rappers\, one is able to apply ACID properties to processes (transactions) in Hadoop.\n

\n\n

\nKatie McMillan leads strategic and creative projects that advance the public health\, information technology\, and soc ial sectors. She currently works for a multi-site Canadian hospital\, and is an advisor to local\, national and international healthcare and public interest initiatives. Katie brings passion and talent for new now know-how and engaging a diverse range of stakeholders to co-create solutions to co mplex organizational\, industry and systems challenges. She leverages a mu lti-disciplinary background in Health Systems\, Geographic Information Sys tems\, Public Policy\, and Data Science\, and works with communities to de sign\, select\, implement\, and monitor solutions in order to improve outc omes with an emphasis on multi-intervention approaches\, interoperability\ , mixed methods\, and Ubuntu. She has worked for organizations including H ealth Canada\, the Canadian Institute for Health Information\, and the Roy al College of Physicians and Surgeons of Canada.\nAdditional information:\ n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Apache Spark Development Lifecycle at Workday LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nEren Avsarogullari\, Pavel Hardak\n \n

\nApache Spark is the backbone of Workday's Prism Analytics Platform\, supporting various data processing use-cases such as Data Inges tion\, Preparation(Cleaning\, Transformation & Publishing) and Discovery. At Workday\, we extend Spark OSS repo and build custom Spark releases cove ring our custom patches on the top of Spark OSS patches. Custom Spark rele ase development introduces the challenges when supporting multiple Spark v ersions against to a single repo and dealing with large numbers of custome rs\, each of which can execute their own long-running Spark Applications. When building the custom Spark releases and new Spark features\, dedicated Benchmark pipeline is also important to catch performance regression by r unning the standard TPC-H & TPC-DS queries against to both Spark versions and monitoring Spark driver & executors' runtime behaviors before producti on. At deployment phase\, we also follow progressive roll-out plan leverag ed by Feature Toggles used to enable/disable the new Spark features at the runtime. As part of our development lifecycle\, Feature Toggles help on v arious use cases such as selection of Spark compile-time and runtime versi ons\, running test pipelines against to both Spark versions on the build p ipeline and supporting progressive roll-out deployment when dealing with l arge numbers of customers and long-running Spark Applications. On the othe r hand\, executed Spark queries' operation level runtime behaviors are imp ortant for debugging and troubleshooting. Incoming Spark release is going to introduce new SQL Rest API exposing executed queries' operation level r untime metrics and we transform them to queryable Hive tables in order to track operation level runtime behaviors per executed query. In the light o f these\, this session aims to cover Spark feature development lifecycle a t Workday by covering custom Spark Upgrade model\, Benchmark & Monitoring Pipeline and Spark Runtime Metrics Pipeline details through used patterns and technologies step by step.\n

\n\n

\nEren Avsarogullari
\ nEren Avsarogullari holds both B.Sc & M.Sc. degrees in Electrical & Electr onics Engineering. Currently\, he works at Workday on Data Analytics as Da ta Engineer. His current focus are Apache Spark internals and Distributed System challenges. He is also open source contributor and member at Apache Software Foundation (Contributed Projects: Apache Spark\, Pulsar\, Heron) .
\nPavel Hardak
Pavel is Director of Product Management with Wo rkday. He works on Prism Analytics product\, focusing on backend technolog ies\, powered by Hadoop and Apache Spark. Pavel is particularly excited ab out Big Data\, cloud\, and open source\, not necessarily in this order. Be fore Workday\, Pavel was with Basho\, the company behind Riak\, open-sourc e NoSQL database with Mesos\, Spark and Kafka integrations. Earlier\, Pave l was with Boundary\, which has developed real-time SaaS monitoring soluti on and was acquired by BMC Corp. Before that\, Pavel worked in Product Man agement and Engineering roles\, focusing on Big Data\, Cloud\, Networking and Analytics\, and authored several patents.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Build a reliable\, efficient and easy-to-use service for Apache Sp ark at Uber's scale LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nNan Zhu\, Wei Han\n\n

\n As the global platform supporting 14+ million daily trips\, Uber leverages huge amounts of data to power decisions like pricing\, routing\, etc. Spa rk is the backbone of large-scale batch computing at Uber. Nearly 250K+ Sp ark applications serve in scenarios like data ingestion\, data cleaning/tr ansformation\, and machine learning model training/inference\, etc. on a d aily basis. Running Spark as a service at Uber’s scale faces many challeng es. (a) reliability is the top priority for us while there are many factor s that could cause outages and negative business impact. We build an end-t o-end robust solution from job service to the Spark distro as well as the thoughtfully designed integrations with other components in data infrastru cture. (b) centralized management of Spark applications is also crucial at Uber’s scale. In the past 2-3 years\, the Spark ecosystem in Uber has bee n evolving from a partially managed situation to a service with full-fledg ed functionalities like monitoring\, version management\, etc. (c) Process ing the massive volume of data in Uber raises challenges against the effic iency of Spark framework itself. We have developed optimizations for neste d column pruning\, parallel hive table committing implementation\, etc. to significantly improve the Spark application performance. In this talk\, w e will walk through the aforementioned journey in Uber and share the exper iences and lessons learned along the way. We hope that this talk can showc ase how Apache software serves industry worldwide\, help others who face s imilar challenges\, and raise more discussions around the topic.\n

\n\n

\nNan Zhu:
\nNan is Tech Lead of Spark team in Uber. He works on Spark service handling 100s of 1000s of Spark application every day in Uber\, and the internal features which scales Spark to handle massive volu me of data in Uber. He is also PMC member of XGBoost\, one of the most pop ular machine learning library in both industry and academia.
\nWei Ha n:
\nWei Han is an Engineering Manager\, leading a few teams in Uber’ s data platform org\, including Spark platform\, Data security and Complia nce\, Privacy platform\, and File Format(Apache Parquet)\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Project Optimum: Spark Performance at LinkedIn Scale LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nYuval Degani\n\n

\nA typ ical day for Spark at LinkedIn means running 35 million RAM-GB-hours\, on top of 7 million CPU-core-hours of 20\,000 unique applications. Maintainin g predictable performance and SLAs\, as-well-as optimizing performance acr oss a complex infrastructure stack and a massive application base\, all wh ile keeping up with a 4x YoY workload growth is an immense challenge. Proj ect Optimum addresses those needs by providing a set of performance analys is\, reporting\, profiling\, and regression detection tools designed for a massive-scale production environment. It allows platform developers as-we ll-as application developers to detect even subtle regressions or improvem ents in application performance with a limited sample set\, using a statis tically-rigorous approach that can be automated and integrated into produc tion monitoring systems. In this talk\, we will cover how Project Optimum is used at LinkedIn to scale our Spark infrastructure while providing a re liable production environment. We will demonstrate its role as an ad-hoc p erformance debugging and profiling tool\, an automatic regression detectio n tracking pipeline\, and an elaborate reporting system geared towards pro viding users with insights about their jobs.\n

\n\n

\nYuval is a Staff Software Engineer at Linkedin\, where he is focused on scaling and d eveloping new features for Hadoop and Spark. Before that\, Yuval was a Sr. Engineering Manager at Mellanox Technologies\, leading a team working on introducing network acceleration technologies to Big Data and Machine Lear ning frameworks. Prior to his work in the Big Data and AI fields\, Yuval w as a developer\, an architect\, and later a team leader in the areas of lo w-level kernel development for cutting-edge high-performance network devic es. Yuval holds a BSc in Computer Science from the Technion Institute of T echnology\, Israel.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Secure your Big Data Cloud cluster with SDX (Ranger\, Atlas\, Knox \, HMS) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDeepak Sharma\n\n

\nSecu rity is very important aspect whether it is onprem or cloud infrastructure . we are here to discuss about how can we secure our DWX/Data science/Data mart cluster with SDX/Data lake. Data Lake security and governance is man aged by a shared set of services referred to as a Data Lake cluster. A Dat a Lake cluster includes the following services: Hive MetaStore (HMS) -- ta ble metadata Apache Ranger -- fine-grained authorization policies\, auditi ng Apache Atlas -- metadata management and governance: lineage\, analytics \, attributes Apache Knox: Authenticating Proxy for Web UIs and HTTP APIs -- SSO IDBroker -- identity federation\; cloud credentials this talk will explain how can we secure multiple workload using single/shared datalake a nd what are the configuration we need to take care\, and how each of the a pache open source component like Ranger\, atlas\, knox helps in this case. \n

\n\n

\nDeepak Sharma\, Apache Ranger Committer Senior Software Engineer\, Cloudera.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Extracting Patient Narrative from Clinical Notes : Implementing Ap ache Ctakes at scale using Apache Spark LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDebdipto Misra\n\n

\nPat ient notes not only document patient history and clinical conditions but a re rich in contextual data and are usually more reliable sources of medica l information compared to discrete values in the Electronic Health Record (EHR). For a medium-sized integrated Health System like Geisinger this amo unts to approximately fifty thousand notes each day. For information extra ction on retrospective data\, the volume can run into millions of notes de pending on the selection criteria. This talk describes the journey taken b y the Data Science Team at Geisinger to implement a distributed pipeline w hich uses Apache Ctakes as the Natural Language Processing (NLP) Engine to annotate notes across the entire spectrum of patient care. From re-writin g certain components in the Ctakes engine to architecting data store and p ipeline optimization for a better throughput\, this talk delves into vario us technical difficulties faced while aspiring to truly do NLP at scale on clinical notes. Towards the end\, the talk also demonstrates few usecases and how using Ctakes has helped clinicians and stakeholders to extract pa tient narratives from patient notes using Apache Solr and Banana.\n

\n\ n

\nDebdipto Misra is a Data Scientist with Geisinger Health. Previo usly\, he worked with AOL Inc. as a Platform Engineer in Audience Analytic s and with EMC Corp. as a Systems Engineer. He has worked in the Data Mini ng and Analytics space for over half a decade. He won a fellowship and pre sented the “Evolution of Prosthetics using Pattern Recognition on Ultrasou nd Signals” at the 2014 IEEE Big Data Conference in Washington\, DC. He ha s also published at multiple journals and presented at healthcare conferen ces like HIMSS. Currently\,his main focus is on building capacity planning tools for healthcare organizations for bed-supply demand using various de ep learning approaches and integrating it with patient notes.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-W1935@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Zeus: Uber’s Highly Scalable and Distributed Shuffle as a Service LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMayank Bansal\, Bo Yang\n\ n

\nZeus is an efficient\, highly scalable and distributed shuffle as a service which is powering all Data processing (Spark and Hive) at Uber. Ub er runs one of the largest Spark and Hive clusters on top of YARN in indus try which leads to many issues such as hardware failures (Burn out Disks)\ , reliability and scalability challenges. Zeus is built ground up to suppo rt hundreds of thousands of jobs and millions of containers which shuffles petabytes of shuffle data. Zeus has changed the paradigm of current exter nal shuffle which resulted in far better performance for shuffle. Although the shuffle data is getting written Remote\, the performance is better or the same for most of the Jobs. In this talk we’ll take a deep dive into t he Zeus architecture and describe how it’s deployed at Uber. We will then describe how it’s integrated to run shuffle for Spark\, and contrast it wi th Spark’s built-in sort-based shuffle mechanism. We will also talk about future roadmap and plans for Zeus.\n

\n\n

\nMayank Bansal:
\ nMayank Bansal is currently working as a Staff engineer at Uber in data in frastructure team. He is co-author of Peloton. He is Apache Hadoop Committ er and Oozie PMC and Committer. Previously he was working at ebay in hadoo p platform team leading YARN and MapReduce effort. Prior to that he was wo rking at Yahoo and worked on Oozie.
\nBo Yang:
\nBo is Sr. Softw are Engineer II in Uber and working on Spark team. In the past he worked o n many streaming technologies.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Flink SQL in 2020: Time to show off! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTimo Walther\n\n

\nFour years ago\, the Apache Flink community started adding SQL support to ease and unify the processing of static and streaming data. Today\, Flink runs business critical batch and streaming SQL queries at Alibaba\, Huawei\, Ly ft\, Uber\, Yelp\, and many others. Although the community made significan t progress in the past years\, there are still many things on the roadmap and the development is still speeding up. In the past months\, several sig nificant improvements and extensions were added including support for DDL statements\, refactorings of the type system and the catalog interface\, a s well as Apache Hive integration. Since it is difficult to follow all dev elopment efforts that happen around Flink SQL and its ecosystem\, it is ti me for an update. This session will focus on a comprehensive demo of what is possible with Flink SQL in 2020. Based on a realistic use case scenario \, we'll show how to define tables which are backed by various storage sys tems and how to solve common tasks with streaming SQL queries. We will dem onstrate Flink's Hive integration and show how to define and use user-defi ned functions. We'll close the session with an outlook of upcoming feature s.\n

\n\n

\nTimo Walther is a committer and PMC member of the Apa che Flink project. He studied Computer Science at TU Berlin. Alongside his studies\, he participated in the Database Systems and Information Managem ent Group there and worked at IBM Germany. Timo joined the project before it became part of the Apache Software Foundation. Today he works as a seni or software engineer at Ververica. In Flink\, he is mainly working on the Table & SQL ecosystem.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-R1735@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Stepping towards Bigdata on ARM LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nVinayakumar B\, Liu Sheng\n\n

\nWe find ARM processors in most devices around us always. ARM proce ssors are mostly used today in small devices due to their power efficiency and reduced cost. But until few years back\, ARM processors were found in only small devices. In recent years\, since major OS providers are suppor ting ARM processors\, ARM ecosystem is stepping into server class business as well. Now since major cloud providers started providing instances base d on ARM\, with far less price of-course\, many businesses moving towards using ARM processors for the deployment. Major factors for considering the deployment environment for any business are Cost and Performance. ARM bei ng cheaper (upto ~50%) than x86\, reduces the overall cost of deployment a nd maintainance for horizontally scalable business deployments. So what ab out Bigdata workloads on ARM? Bigdata components are mostly scalable. They will benefit from this model as well\, provided they support deployment o n ARM. This talk\, discusses about such initiative to support deployment a nd optimizations on ARM servers of Major bigdata components and reduce the total cost of deployment and management for the user. This includes contr ibutions done in various apache projects like\, Hadoop\, Spark\, Hive\, HB ase\, Kudu\, Impala\, to support deployments on ARM nodes and optimization s for ARM processor. Finally\, shows some benchmark results in ARM and x86 deployments.\n

\n\n

\nVinayakumar B:
\nVinayakumar B\, havi ng vast experience with Hadoop for 10+ years. Focuses on improvements in a nd around Hadoop and Big-Data. Contributing to Hadoop community from 8+ ye ars\, and currently a Committer and member of Apache Hadoop PMC.
\nLi u Sheng:
\nLiuSheng\, Focusing on promoting opensource projects in Bi g-Data components running on ARM platform. Contributed towards building AR M CI for Hadoop community and others. Currently working on ARM support for Kudu project\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-R1815@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Managing Transaction on Ethereum with Apache Airflow LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Ghen\n\n

\nApach e Airflow is a Python-based workflow management system that can be used to actively monitor and execute transactions on blockchain networks like Eth ereum. This presentation is an introduction to Apache Airflow followed by a demonstration of a production deployment. Apache Airflow is an excellent tool for anyone already familiar with Python. Its ability to process jobs and handle errors makes it a good choice tool for managing activity on bl ockchain networks. The goal of this talk is to demonstrate how Apache Airf low can be used for environmental scanning and batch processing transactio ns. The demonstration will cover using Airflow and Python for monitoring a nd executing ERC20 token transactions on the Ethereum blockchain.\n

\n\ n

\nMichael Ghen is a computer engineer from Philadelphia that has c ontributed to Apache Airflow and Apache Unomi. He has a B.S. in computer e ngineering from Pennsylvania State University and an M.S. in analytics fro m Brandeis. Currently\, he is a GAANN Cybersecurity Fellow at Drexel where he is pursuing a Ph.D. in electrical engineering. He previously served as a data architect and engineer at start-ups and non-profits where he used Apache Airflow to build data pipelines.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-R1855@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:After NoSQL discover CloudSQL databases LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRomain Manni-Bucau\, Enrico Olivell i\n\n

\nApplication without persistence are rare and since some years the persistence layer is changing a lot. After years where SQL data bases where the only ones\, we saw NoSQL popping up bringing new concepts. More recently\, the cloud changed again our paradigms with distributed co mputing and microservices. However\, even with these brand new solutions\, we still lack the flexibility of the SQL in terms of evolutivity and tool ing. This is where HerdDB is entering into the game. Built as a SQL databa se\, its foundations are Apache BookKeeper (bookie for close friends) and Apache Calcite. Therefore it brings to our architecture new solutions. Thi s talk will first go through the challenges which led to creating HerdDB\, then how it is designed and why it merges the best of both NoSQL and SQL worlds and finally we will illustrate its usage by two applications using very different deployment modes (from plain old bare metal to Kubernetes) using Apache Meecrowave and Geronimo Microprofile Stack.\n

\n\n

\ nRomain Manni-Bucau:
\nJoined the Apache EE family (OpenWebBeans\, Me ecrowave\, Johnzon\, BatchEE...) in 2011. My goal is to make development a detail of an idea becoming reality.
\nEnrico Olivelli:
\nSoftwa re Developer Manager at https://MagNews.com and https://EmailSuccess.com. PMC in Apache BookKeeper\,ZooKeeper\,Curator\, Committer in Maven. OpenSou rce/ASF Enthusiast Initial creator of other OpenSource projects:HerdDB\,Bl azingCache\,BlobIt\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-bigdata-2-R1935@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20201001T193500Z DTEND:20201001T201500Z SUMMARY:Apache Big-Data meets Cloud-Native and Kubernetes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMárton Elek\n\n

\nApache big-data projects / the Hadoop ecosystem is widely adopted and very popul ar so the Kubernetes / Cloud-native tools. Surprisingly there are only a v ery minimal number of projects in the intersection of the two words. This presentation explains why could it be\, shows the key problems to run Apac he Big-Data projects (such as Hadoop\, Kafka\, Flink\, Spark...) on Kubern etes and gives a demo of a possible solution.\n

\n\n

\nMarton Ele k is PMC in Apache Hadoop and Apache Ratis projects and working on the Apa che Hadoop Ozone at Cloudera. Ozone is a new Hadoop sub-project which prov ides an S3 compatible Object Store for Hadoop on top of a new generalized binary storage layer. He is also working on the containerization of Hadoop and creating different solutions to run Apache Big Data projects in Kuber netes and other could native environments.\n

CATEGORIES:Big Data Track (2) URL;VALUE=URI:https://apachecon.com/acah2020/tracks/bigdata-2.html#R1935 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143450Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:What's new with Apache Camel 3? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrea Cosentino\, Claus Ibsen\n\n

\nWith the release of Apache Camel 3\, the Camel family has bee n extended to include a full range of projects that are tailored to popula r platforms including Spring Boot\, Quarkus\, Kafka\, Kubernetes\, and oth ers\; creating an ecosystem. Lets discover it.\n

\n\n

\nAndrea Co sentino:
\nAndrea Cosentino (@oscerd on Github and @oscerd2 on Twitte r) is an open-source addicted and software developer. He’s co-leading Apac he Camel and he’s actually the PMC Chair of the project. He’s currently wo rking on expanding the Camel ecosystem through new subprojects like Camel- k\, Camel-Quarkus and Camel-Kafka-connector (the latest project in the fam ily). Andrea is active on multiple projects like Apache Karaf\, where he i s committer\, Apache Servicemix\, where he is PMC Member\, Fabric8 Kuberne tes-client\, where he is a core maintainer and many others. Andrea is acti ve on social media and blogs\, trying to spread the word about Apache Came l and open-source in general. He is actually Senior Software Engineer in R ed Hat\, working in the Red Hat Fuse team\, focusing on integration. He is based in Rome\, Italy\, where he lives with his wife and son.
\nClau s Ibsen:
\nClaus Ibsen (@davsclaus) is an open-source enthusiast and software developer. He's co-leading the Apache Camel project\, a project u sed for integration\; which he has been working on full time for more than a decade. Currently Claus is working on expanding Camel into cloud-native and serverless with the latest innovations of Apache Camel K and Camel Qu arkus. With passion and enthusiasm Claus evangelizes about Apache Camel\, Java and open source by being active on social media\, writing blogs and b ooks\, speaking at conferences\, etc. Claus is also active in other open s ource projects such as Apache ActiveMQ\, Eclipse Vert.x\, Fabric8\, Hawtio \, and Quarkus. Besides being a JavaChampion\, Claus is also a member at A pache Software Foundation. Prior to joining Red Hat\, he has worked as a s oftware developer\, architect\, and consultant for over a decade. He is ba sed in Denmark.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143450Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Making Enterprise Integration Patterns Work for You LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJustin Reock\n\n

\nLearn about the powerful world of Enterprise Integration Patterns as implemente d by the amazing Apache Camel framework. This session covers:
\nA – C amel basics\, understanding Exchanges\, Routes\, and how to implement EIPs with them
\nB – Examples of real implementations of common EIPs like Content Based Routers and Recipient Lists
\nC – Integration of Camel with common endpoints\, like JMS\, FTP\, and HTTP\n

\n\n

\nJusti n has over 20 years’ experience working in various software roles and is a n outspoken free software evangelist\, delivering enterprise solutions and community education on databases\, integration work\, architecture\, and technical leadership. He is currently the Chief Architect at OpenLogic by Perforce.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143450Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Getting started with Apache Camel on Quarkus LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAlexandre Gallice\n\n

\n Apache Camel is the proven Swiss knife of integration for more than a deca de and still growing in the cloud era. As a Java based framework\, it make s perfect sense for Camel to reap the benefit from Quarkus\, the Kubernete s Java stack tailored for OpenJDK HotSpot and GraalVM. In this hands on de mo\, I will show what it looks like to develop with Camel Quarkus. One cou ld expect to take away some key concepts and maybe a willingness to join t he lively Camel community :)\n

\n\n

\nAlexandre is a Senior Softw are Engineer at Red Hat and a member of the Apache Camel Project Managemen t Committee. He is deepening his interest in Open Source for a few years n ow with a current focus on Camel Quarkus.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Build and Deploy Cloud Native Camel Quarkus Routes With Tekton and \nKnative LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nOmar Al-Safi\n\n

\nIn th is talk\, we will leverage all cloud native stacks and tools to build Came l Quarkus routes natively using GraalVM native-image on Tekton pipeline an d deploy these routes to Kubernetes cluster with Knative installed. We wil l dive into the following topics in the talk: - Introduction to Camel - In troduction to Camel Quarkus - Introduction to GraalVM Native Image - Intro duction to Tekon - Introduction to Knative - Demo shows how to deploy end to end a Camel Quarkus route which include the following steps: - Look at whole deployment pipeline for Cloud Native Camel Quarkus routes - Build Ca mel Quarkus routes with GraalVM native-image on Tekton pipeline. - Deploy Camel Quarkus routes to Kubernetes cluster with Knative Targeted Audience: Users with basic Camel knowledge\n

\n\n

\nOmar Al-Safi is an Ope n Source Software Engineer at Talend. He is an active Apache Camel contrib utor and Apache Camel PMC. His experience before contributing to Apache Ca mel\, revolved around building streaming platforms with Kafka\, Kafka Stre ams as well as contributing to Debezium project.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-T1855@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Camel Kafka Connectors: when camel meets kafka LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrea Tarocchi\, Hugo Guerrero\n\n

\nApache Kafka is getting used as an event backbone in new org anizations every day. We would love to send every byte of data through the event bus. However\, most of the time\, connecting to simple third party applications and services becomes a headache that involves several lines o f code and additional applications. As a result\, connecting Kafka to serv ices like Google Sheets\, communication tools such as Slack or Telegram\, or even the omnipresent Salesforce\, is a challenge nobody wants to face. Wouldn’t you like to have hundreds of connectors readily available out-of- the-box to solve this problem? Due to these challenges\, communities like Apache Camel are working on how to speed up development on key areas of th e modern application\, like integration. The Camel Kafka Connect project\, from the Apache foundation\, has enabled their vastly set of connectors t o interact with Kafka Connect natively. So\, developers can start sending and receiving data from Kafka to and from their preferred services and app lications in no time without a single line of code. In summary\, during th is session we will: - Introduce you to the Camel Kafka Connector sub-proje ct from Apache Camel - Go over the list of connectors available as part of the project - Showcase a couple of examples of integrations using the con nectors - Share some guidelines on how to get started with the Camel Kafka Connectors\n

\n\n

\nAndrea Tarocchi:
\nAndrea Tarocchi is a Senior Software Engineer at Red Hat. He has been solving application inte gration problems for more than 10 years spanning different roles. Co-creat or of camel-kakfa-connector project\, Apache Camel committer. He is a long time opensource enthusiast been lucky enough to work with and contribute to some great open source projects\, like Apache Camel\, Apache Kafka\, Dr ools to mention a few.
\nHugo Guerrero:
\nHugo Guerrero works at Red Hat as an APIs and messaging developer advocate. In this role\, he he lps the marketing team with technical overview and support to create\, edi t\, and curate product content shared with the community through webinars\ , conferences\, and other activities. With more than 15 years of experienc e as a developer\, consultant\, architect\, and software development manag er\, he also works on open source software with major private and federal public sector clients in Latin America\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Integrating Postgres with Apache Camel and ActiveMQ LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJustin Reock\n\n

\nLearn how to use Postgres as a backing persistence adapter for the ActiveMQ mes saging platform\, as well as an integration endpoint for the powerful Apac he Camel integration framework. Not only will you learn about JDBC\, but y ou'll also get a solid introduction to these two mature and powerful integ ration platforms.\n

\n\n

\nJustin has over 20 years’ experience w orking in various software roles and is an outspoken free software evangel ist\, delivering enterprise solutions and community education on databases \, integration work\, architecture\, and technical leadership. He is curre ntly the Chief Architect at OpenLogic by Perforce.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-W1615@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Camel API Gateway LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRodrigo Coelho\n\n

\nOpe n Source light API Gateway built with Apache Camel
\nRecently I was c hallenged to find alternatives to the existing API Gateway infrastructure.
\nNot being able to find any solution with all we need to offer\, Ap ache Camel was the perfect candidate.
\nWe called it CAPI Gateway!
\nCAPI provides the following features: Light API Gateway powered by Ap ache Camel and Spring Boot\, Dynamic Routes (REST and Websockets)\, Custo mizable processors\, Integration with external Identity Providers (Default is Keycloak)\, API Manager Interface\, distributed tracing system (Zipkin )\, Metrics (Prometheus)\, API Subscription Engine (Keycloak)\, Traffic ma nagement (Apache Camel Kafka)\, analytics for the metrics (Grafana) and Er ror/Blocking strategies.\n

\n\n

\nRodrigo is a Software Architect and Technology lover particularly focus on Open Source technology and Dev Ops\, and how we can apply this new paradigms to the real and challenging enterprise world. With more than 17 years of experience\, I've always made a huge effort to stay on top of the big changes in the software industry\ , not only on the software itself but also on paradigm. Since 2013 I've be en working as a software and solution architect\, giving my contribution t o companies like bpost (Belgium) and AXA Bank (Belgium). On this last proj ect at AXA I was responsible for the design and implementation from scratc h of a strategic application. Since January I've been working as a Softwar e Architect \, Technology Expert and Open Source advocate for the Reusable Components Office at the European Commission.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-W1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:How to contribute textual tooling for Apache Camel in several IDEs LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAurélien Pupier\n\n

\nAp ache Camel allows to configure Integration projects using several textual Domain Specific Languages. In this talk\, you will learn how tooling is pr oposed for several IDEs and editors thanks to the Language Server Protocol and its implementation for Apache Camel language. Entry points to allow y ou to join the party and contribute will be presented.\n

\n\n

\nA urélien is working in Red Hat Integration Tooling team. Developing tooling targeting developers for more than 10 years.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-W1735@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Serverless Integration Anatomy LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nChristina Lin\n\n

\nA qu ick study of the structure on building Serverless Integration. Piecing tog ether how Kubernetes\, Knative\, Kafka and Apache Camel. Going over the li fecycle of a serverless application\, from development\, deployment to mon itoring it live. Talk about things to consider when building this type of architecture. At the end a quick demo to show how it works.\n

\n\n

\nChristina Lin is the Technical evangelist for Red Hat Integration Prod ucts. She helps to grow market awareness and establish thought leadership for Fuse\, AMQ and 3scale. By creating online videos\, getting started blo gs and also spoke at many conference around the globe. She has worked in s oftware integration for the finance\, telecom\, and manufacturing industri es\, mostly architectural design and implementation. These real life syste m experiences helps her to be practical and combining open source technolo gy\, she hopes to bring more innovative ideas for the future system develo pment.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Testing Camel K integrations with Cloud Native BDD LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nChristoph Deppisch\n\n

\ nApache Camel K is a lightweight integration platform built from Apache Ca mel. Integrations built with Camel K run natively on Kubernetes and are sp ecifically designed for serverless architectures. With the declarative nat ure in Camel K users can instantly run integration code written in Camel D SL on their preferred cloud. The presentation outlines typical integration scenarios with Camel K and shows how to write automated tests for these e nterprise integrations. The session covers classical service provider/cons umer scenarios with common messaging protocols (e.g. REST\, JMS\, Kafka) a s well as more complex integrations with data access and 3rd party Saas se rvices included. The tests itself will also be Cloud Native citizens and m ake use of Behavior Driven Development concepts.\n

\n\n

\nChristo ph is a senior software engineer at Red Hat working on Middleware applicat ion services with Apache Camel. He has worked in enterprise integration pr ojects for more than 10 years and has gained special interest in test auto mation. Christoph is the founder of the Open Source test framework Citrus and believes in automated integration testing with passion.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-W1855@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:\"Cloud Native\" My Camel LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Costello\, David Gordon \n< /strong>\n

\nThis talk takes a look at our traditional enterprise integr ation needs\, how we have typically solved them with Enterprise Integratio n Patterns (EIP) via Apache Camel (running in Apache Karaf) and how to use a new way of deploying Camel (and ultimately our enterprise integration s olutions) with Camel K to put enterprise integration on \"Cloud Native\" s teroids. During the talk we'll discuss a typical integration performed usi ng Enterprise Integration Patterns with Apache Camel\, why approaching pro blems with this technology is even more valuable in our brave new cloud wo rld\, and demonstrate how to put this on cloud native steroids with Camel K.\n

\n\n

\nMike has spent the last 2 decades in the enterprise i ntegration space. Beginning with his love for J2EE\, emerged a love for Se rvice Oriented Architecture and as the years carried on his romance with M icroServices and cloud native distributed integration platforms began to r eally flourish. Mike\, spent his college years at the University of Texas and currently works for Red Hat as an Architect in an Emerging Techonology Practice (Enterprise Integration) of Red Hat Consulting. His views may or may not be shared by his employer (or anyone else for that matter). When not swashbuckling with containers\, or integrating event streams\, Mike ma y be found kicking a soccer ball around the Austin\, Texas area.\n
\n David helps organizations use open source software to implement integratio n solutions. David specializes in open source including Camel\, Spring Boo t\, Kubernetes\, ActiveMQ\, Enmasse\, Kafka\, Strimzi\, 3scale\, Keycloak\ , Istio\, and more. David helps design and develop implementations using t hese components\, and leverage those experiences to develop feedback for e ngineering groups he works with and upstream development communities.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-W1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Software Architecture and Architectors: useless VS valuable LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrei Shakirin\n\n

\nTa lk introduces definitions and sense of system architecture. Presenter will show seven cases from real projects\, where wrong\, missing or over-sophi sticated architecture decisions really hurt the development teams. The res cue solution and lesson learned will be presented for each situation. The presenting cases and solutions are related to Apache Projects: Apache Kara f\, CXF\, Camel\, Kafka. Open discussion and own cases and project situati ons are welcome.\n

\n\n

\nAndrei is a software architect in the T alend team developing the open source Application Integration platform bas ed on Apache projects. The areas of his interest are REST API design\, Mic roservices\, Cloud\, resilient distributed systems\, security and agile de velopment. Andrei is PMC and committer of Apache CXF and committer of Sync ope projects. He is member of OASIS S-RAMP Work Group and speaker at Java and Apache conferences.

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-R1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Panel on the future of Software Integration LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMaria Arias de Reyna Dominguez\n\n

\nPanel on the future of Software Integration.\n

\n\n

\nMaría Arias de Reyna is a Java Senior Software Engineer\, geospatial ent husiast and Open Source advocator. She has been a community leader and cor e maintainer of several free and open source projects since 2004. She is c urrently working at Red Hat where she focuses on Middleware and maintains Apache Camel and Syndesis. María is an experienced keynoter and speaker. B etween 2017 and 2019 María was the elected President of OSGeo\, the Open S ource Geospatial Foundation which serves as an umbrella for the most used geospatial free and open source software. She is also well known as a femi nist and Women In Tech activist.\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-camel-R1735@apachecon.com SEQUENCE:1 DTSTAMP:20200812T151913Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Camel Lightning Talks LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nYou! Any speaker is welcome!\n\n

\nCurrently submitted lightning talks are shown here on the left\ , submit your lightning talk using the form on the right. Please allow the form to load fully.\n

\n\n\n

\n\n

\n

CATEGORIES:Camel/Integration URL;VALUE=URI:https://apachecon.com/acah2020/tracks/camel.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T0930@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T093000Z DTEND:20200929T101000Z SUMMARY:Lessons Learned: Building Cassandra DBaaS on Alibaba Cloud LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMaxwell Guo\n\n

\nDuring this session\, we will share the lessons we learned when we built Apache Cassandra as a Service on Alibaba Cloud. Specific topics include: - how we boosted Cassandra performance through soft raid on cloud disk\, - why we do a continuous full incremental backup - how to apply automatic data repa irs Additionally\, we will share the experience of doing non-stop data mig ration between different Cassandra clusters and between Cassandra and othe r databases as well as how we optimize a Cassandra service for different u se case.\n

\n\n

\nMaxwell is a cloud software architect at Alibab a\, working on offering Apache Cassandra as a cloud based service.\n< /p> CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T0930 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Towards Practical Self-Healing Distributed Databases LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDinesh Joshi\, Joey Lynch\n\n

\nAs distributed databases expand in popularity\, there is ever-grow ing research into new database architectures that are designed from the st art with built-in self-tuning and self- healing features. In real world de ployments\, however\, migration to these entirely new systems is impractic al and the challenge is to keep massive fleets of existing databases avail able under constant software and hardware change. Apache Cassandra is one such existing database that helped to popularize \"scale-out\" distributed databases and it runs some of the largest existing deployments of any ope n-source distributed database. In this talk\, we demonstrate the technique s needed to transform the typical\, highly manual\, Apache Cassandra deplo yment into a self-healing system. We start by composing specialized agents together to surface the needed signals for a self-healing deployment and to execute local actions. Then we show how to combine the signals from the agents into the cluster level control- planes required to safely iterate and evolve existing deployments without compromising database availability . Finally\, we show how to create simulated models of the database's behav ior\, allowing rapid iteration with minimal risk. With these systems in pl ace\, it is possible to create a truly self-healing database system within existing large-scale Apache Cassandra deployments.\n

\n\n

\nDine sh Joshi:
\nDinesh A. Joshi has been a professional Software Engineer for over a decade building highly scalable realtime Web Services and Dist ributed Streaming Data Processing Architectures serving over 1 billion dev ices. Dinesh is an active contributor to the Apache Cassandra codebase. He has a Masters degree in Computer Science (Distributed Systems & Databases ) from Georgia Tech\, Atlanta\, USA.
\nJoey Lynch:
\nJoey helps keep the wheels on the bus for Netflix’s data infrastructure.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Building Apache Cassandra 4.0: behind the scenes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDinesh Joshi\n\n

\nBuild ing a database is hard. Building a distributed database is harder. Buildin g a distributed database that the industry relies on is even harder. Our g oal to build Apache Cassandra 4.0 is to make it rock solid. In this talk\, we go behind the scenes to show you how the Apache Cassandra community is building and testing Apache Cassandra 4.0 so that it is the most stable r elease ever!\n

\n\n

\nDinesh A. Joshi has been a professional Sof tware Engineer for over a decade building highly scalable realtime Web Ser vices and Distributed Streaming Data Processing Architectures serving over 1 billion devices. Dinesh is an active contributor to the Apache Cassandr a codebase. He has a Masters degree in Computer Science (Distributed Syste ms & Databases) from Georgia Tech\, Atlanta\, USA.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:5 Ways to Solve Cassandra GC Problems LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCaroline George\n\n

\nGa rbage Collection can be painful\, impact performance and stability\, and c an even take down entire clusters. In this talk\, we will start by going o ver the 5 most common reasons for GC in Apache Cassandra. Then we will dis cuss ways to address these issues. And end with how to monitor your cluste r going forward to avoid running into GC problems.\n

\n\n

\nSpent over 6 years working with Apache Cassandra as an SE at Datastax\, Carolin e is now helping customers increase performance and provide stability with their JVM at Azul Systems. Originally from France\, she has spent most of her life in NYC and holds a BA in Computer Science from NYU and MBA from NYU Stern School of Business.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Cloud-Native Cassandra LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPatrick McFadin\n\n

\nKu bernetes is becoming a standard tool to deploy large scale infrastructure and lately\, Apache Cassandra. We'll look at some of the methods used to d eploy Cassandra using Kubernetes including storage options\, networking co nfiguration\, and monitoring. In the past year\, the Apache Cassandra proj ect has also taken on the task of creating a common operator closer to the project. This will be a chance to get the latest status of the operator e ffort and where it will be headed post-Cassandra 4.0.\n

\n\n

\nPa trick McFadin is the VP of Developer Relations at DataStax\, where he lead s a team devoted to making users of Apache Cassandra successful. He has al so worked as Chief Evangelist for Apache Cassandra and consultant for Data Stax\, where he helped build some of the largest and exciting deployments in production. Previous to DataStax\, he was Chief Architect at Hobsons an d an Oracle DBA/Developer for over 15 years.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Getting started with Cassandra the right way LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nErick Ramirez\n\n\n

\nCa ssandra users run into problems particularly when they're new to the techn ology. In this session\, I'll talk about: - the common pitfalls so you don 't fall into the trap\; - top things users ask for help\; - how to quickly diagnose issues\; - where to get help.\n

\n\n

\nI'm an Apache Ca ssandra enthusiast at DataStax. I've been educating and helping other user s become successful with Cassandra for 7 years. I answer questions on vari ous channels including ASF Slack and the users mailing list.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Advanced data modeling techniques for Cassandra LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nArturo Hinojosa\, Michael Raney\n\n

\nWhether your storing timeseries data for a messaging app or device metadata for an industrial IoT application\, your Cassandra data mo del can have a massive impact on your application’s performance and scalab ility. In this talk\, we will walk through advanced techniques and best pr actices for building highly scalable\, fast\, and robust data models. You will learn how to model your data based on your queries and access pattern s to ensure you have well-distributed data that will enable your applicati on to scale up as traffic grows. We will talk through examples of deformal izing data\, modeling complex relationships\, and optimizations that you c an apply to your schemas and data models to improve performance.\n

\n\n

\nArturo Hinojosa:
\nArturo Hinojosa is a Principal Product Ma nager on the Amazon Keyspaces (for Apache Cassandra) team at Amazon Web Se rvices (AWS). Arturo is responsible for Amazon Keyspaces' overall product strategy and has been with AWS for over four years.
\nMichael Raney:< br />\nMichael is the lead specialist solution architect (SA) for Amazon K eyspaces (for Apache Cassandra). As the lead SA for Amazon Keyspaces\, Mic hael works with customers every day to design cloud-based NoSQL solutions for large-scale distributed systems.\n

\n\n\n\n CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-W0900@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:Large scale Cassandra Use Cases and Best Practices at Huawei Consu mer Cloud LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDuican Huang\n\n

\nCassa ndra is widely used in key business scenarios in Huawei Consumer Cloud. Yo u can find Cassandra databases serving as the real-time data store behind almost all Huawei consumer electronic products that are used by billions o f people in China and the rest of the world. With a long history of Cassan dra adoption ever since 2010\, Huawei Consumer Cloud’s Cassandra deploymen ts have grown to 30\,000+ nodes\, supporting more than 10 million operatio ns per second with average latency of 4ms\, and the maximum number of tabl e records reaches 300 billion. Along this journey\, we have gained a lot o f experience in data modeling\, fine-tuning leveled compaction with high n ode density\, day-to-day operations such as repair and handling tombstones \, monitoring and problem identification and quick resolution under very t ight SLA\, which we are thrilled to share with the community. We also summ arized our lessons learned and best practices in managing those low-latenc y\, high-concurrency and mission-critical use cases.\n

\n\n

\nDui can Huang is a Huawei Senior R&D Engineer\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#W0900 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-W0940@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T094000Z DTEND:20200930T102000Z SUMMARY:Making Cassandra more capable\, faster\, and more reliable LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nHiroyuki Yamada\, Yuji Ito\n\n

\nCassandra is widely adopted in real-world applications and used b y large and sometimes mission-critical applications because of its high pe rformance\, high availability and high scalability. However\, there is sti ll some room for improvement to take Cassandra to the next level. We have been contributing to Cassandra to make it more capable\, faster\, and more reliable by\, for example\, proposing non-invasive ACID transaction libra ry\, adding GroupCommitLogService\, and maintaining and conducting Jepsen testing for lightweight transactions. This talk will present the contribut ions we have done including the latest updates in more detail\, and the re asons why we made such contributions. This talk will be one of the good st arting points for discussing the next generation Cassandra.\n

\n\n

\nHiroyuki Yamada:
\nHiroyuki Yamada is CTO and CEO at Scalar\, Inc . He has been passionate about parallel and distributed data management sy stems for more than 15 years. Prior to Scalar\, he worked at IIS UTokyo\, Yahoo\, IBM. Ph.D. from the University of Tokyo.
\nYuji Ito:
\nW orking on distributed database/storage. Formerly\, worked on SSD firmware. Master's degree in Information Science and Technology from The University of Tokyo.\n

\n\n\n\n\n CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#W0940 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:How Netflix Manages Version Upgrades of Cassandra at Scale LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSumanth Pasupuleti\n\n

\ nWe at Netflix have about 70% of our fleet on Apache Cassandra 2.1\, while the remaining 30% is on 3.0. We have embarked on a multi quarter task of upgrading our 2.1 fleet to 3.0\, as part of which we are doing several kin ds of verification overarching both correctness and performance. It is a k nown issue that cross version streaming is not supported in Cassandra. To work around this\, we've also developed a version agnostic upgrade mechani sm using our desire based automation\, to avoid needing to do cross versio n streaming. Through this approach\, we can tolerate loosing a node while the upgrade is in progress and the cluster is in mixed mode of major versi ons. As part of this talk\, I would like to elaborate on what kinds of ver ification we are doing as well as the upgrade mechanism we have developed to avoid cross version streaming.\n

\n\n

\nSumanth Pasupuleti is a Senior Software Engineer at Netflix\, focusing on innovating and operati ng at scale\, both caching and persistent datastore solutions like EVCache and Cassandra\, offered as a platform within Netflix.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Hidden features of Apache Cassandra 4.0 LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDinesh Joshi\n\n

\nApach e Cassandra 4.0 is a huge community effort! It has over 400 patches includ ing features and bug fixes. We have a lot of features that are well known and there are great features that are not so well known. In this talk\, yo u will learn about some of those hidden features that might make your life easier\, give you great performance boost or just surprise you!\n

\n\n

\nDinesh A. Joshi has been a professional Software Engineer for ove r a decade building highly scalable realtime Web Services and Distributed Streaming Data Processing Architectures serving over 1 billion devices. Di nesh is an active contributor to the Apache Cassandra codebase. He has a M asters degree in Computer Science (Distributed Systems & Databases) from G eorgia Tech\, Atlanta\, USA.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Reasoning about Cassandra performance from first principles LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJeff Hajewski\n\n

\nTher e are a plethora of articles and blog posts on Cassandra performance and p erformance tuning. Typically these resources contain specific pieces of ad vice on how to improve read or write throughput. The problem with these re sources is that they focus on a specific solution to specific problem. In this talk we will start from first principles and develop a mental model t hat will allow us to reason about Cassandra's performance. The goal of the talk is for attendees to leave with a deeper understanding of how Cassand ra works and how they can use that information to think through Cassandra' s performance characteristics. We will start off by looking at how Cassand ra stores data\, the underlying data structures\, and the implications of these design choices. The next two parts of the talk will discuss how Cass andra handles reads and writes and the associated trade-offs in the contex t of distributed systems. This talk is suitable both for those that regula rly use Cassandra as well as those who are new to Cassandra because we foc us on the ideas and principles behind Cassandra\, rather than specific API s or configurations.\n

\n\n

\nJeff is a software engineer at Sale sforce\, where he works on distributed systems for machine learning on str eaming data. Prior to working at Salesforce he did his PhD at the Universi ty of Iowa. He works remotely from Iowa\, where he lives with his wife\, k id\, and dog.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-W1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Cassandra Upgrade in production : Strategies and Best Practices LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nLaxmikant Upadhyay\n\n

\ nThis session will cover how to perform Cassandra cluster upgrade in produ ction effectively. We will learn about best practices for planning & execu ting Cassandra upgrades. We will also discuss and understand different Cas sandra upgrade strategies and their respective pros & cons so that Operati ons team can select the appropriate strategy. Finally\, we will talk about standard upgrade issues and how we have created custom solutions at Erics son to overcome those issues. The session is useful for Cassandra Operator s\, Administrators and other Cassandra users involved in planning\, perfor ming and testing upgrades.\n

\n\n

\nLaxmikant Upadhyay is an Apac he Cassandra enthusiast with over 10 years of experience in developing mut liple distributed scalable and HA software solutions. Currently\, he works as Sr. Data engineer (nosql) and Cassandra SME with American Express R&D. He is core contributor of open source Cassandra Audtiing plugin ecaudit . He has designed and implemented multiple distributed\, fault tolerant\, s calable and HA software systems. He has helped many teams in designing eff icient and scalable data model and performance tuning of C*.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-R1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Getting Involved with the Apache Cassandra Project LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nEkaterina Dimitrova\n\n

\nThey say it’s always hard the first time you do something. Is it really? In this talk we prove the opposite and give some guidance on how to simpl ify the way new open-source contributors learn and contribute for the firs t time to a project like Apache Cassandra. Contributions can happen in man y forms\, from documentation\, testing and bug fixing to developing new co ol features. Come\, join us in our exciting adventure to Cassandra 4.0\, t he most stable release ever\, and beyond to 5.0!\n

\n\n

\nCassand ra contributor and distributed systems deva.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-R1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Containerized Cassandra Cluster (CCC) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nStanislav Kelberg\n\n

\n Elegant and fully controllable Cassandra cluster for local testing and dev elopment. A modern and robust alternative to ccm (Cassandra Cluster Manage r)\, taking advantage of containers\, while keeping the full control of Ca ssandra configuration. This talk will demonstrate how to easily test local ly against a cluster with production like features\, for example: multi DC \, SSL\, Authentication etc.\n

\n\n

\nStan is a seasoned DevOps e ngineer who has worked for small startups and large enterprises like Deuts che Bank and Sky. Stan has been heavily involved with Cassandra and DSE in the last 6 years\, 4 of which he has worked for digilalis.io\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-R1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Re-imaging Cassandra authentication using short-term credentials LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nArturo Hinojosa\, Derek Chen-Becker \, Brian Houser\n\n

\nApache Cassandra manages access by using traditional usernames and passwords. However\, organizations and developer s are moving towards more secure access management techniques for programm atic access\, such as using short-term credentials. In this talk\, we will dive deep on how Amazon Web Services (AWS) designed and built an open-sou rce authentication plugin for Cassandra drivers that enables developers to use short term credentials for access management instead of hard-coding c redentials in their application code. You will learn how the plugin integr ates with Cassandra drivers and how the security model works in comparison to traditional authentication.\n

\n\n

\nArturo Hinojosa:
\n Arturo Hinojosa is a Principal Product Manager on the Amazon Keyspaces (fo r Apache Cassandra) team. Arturo is responsible for the overall product st rategy of Amazon Keyspaces and has been with Amazon Web Services (AWS) for over four years.
\nDerek Chen-Becker:
\nDerek Chen-Becker is a senior software development engineer on the Amazon Keyspaces (for Apache C assandra) team. Derek is the original author of the AWS authentication plu gin for Apache Cassandra drivers. Derek is interested in network engineeri ng and enterprise software development\, with focuses in distributed syste ms\, monitoring and management.
\nBrian Houser:
\nBrian Houser i s a Senior Software Development Engineer on the Amazon Keyspaces (for Apac he Cassandra) team. Brian leads open-source efforts for Amazon Keyspaces a nd has been with Amazon for more than 10 years.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-R1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Upgrading Cassandra using Automation\, with cstar LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nValerie Parham-Thompson\n\ n

\nI recently did an upgrade of 200+ nodes of Cassandra across multiple environments sitting behind multiple applications using the cstar tool. W e chose the cstar tool because\, out of all automation options\, it has to pology awareness specifically to Cassandra. I will share my experience wit h this upgrade\, including observations and surprises\, as well as a walk- through of the process using a Cassandra cluster provisioned in Docker.\n< /p>\n\n

\nWith experience as an open-source DBA and developer for so ftware-as-a-service environments\, Valerie has expertise in web-scale data storage and data delivery\, including MySQL\, Cassandra\, Postgres\, and MongoDB.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-R1855@apachecon.com SEQUENCE:2 DTSTAMP:20200810T213949Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:Hadoop as a Cassandra SSTables producer LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSerban Teodorescu\, Adelina Vidovic i\n\n

\nWe’re using a lambda architecture\, with Hadoop used fo r the main database and Cassandra deployed as persistent cache at edges\, in total about 7-800 Cassandra nodes. One issue is daily push of data from Hadoop to Cassandra\, which is the main factor that impacts the clusters performance and costs. We used to produce JSON data in Hadoop\, then conve rt it to SSTables at the edges and streaming them to Cassandra. I’ll show why this architecture is unable to take advantage of Cassandra 4 streaming improvements\, why is that important for us\, how to combine Hadoop with Cassandra vnodes in order to achieve optimal streaming\, and show some (pr eliminary) performance figures. The later is work in progress\, but I hope it will be finished by the time the conference is startin\n

\n\n

\nSerban Teodorescu
\nI'm at SRE at Adobe\, part of a small team tha t manages 30+ Cassandra clusters for Adobe Audience Manager. Previously\, I was a Python programmer\, and I'm still trying to find out how a softwar e developer who preferred SQL databases ended up as an SRE for a Cassandra team\, and then started to work in Java.
\nAdelina Vidovici
\nI 'm Software Engineer in Adobe Romania with a background in Computer Scienc e and a big passion for Chemistry.\nIn the last 2.5 years\, I was part of the Adobe Audience Manager team and I’ve got the chance to learn and work with Big Data technologies: Trust me! We have cookies! :)\nBesides work\, I enjoy reading\, travelling and going for a bike ride from time to time.\ n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-cassandra-R1935@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T193500Z DTEND:20201001T201500Z SUMMARY:Truth Hurts: How to Migrate your Data Model to Apache Cassandra LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAmanda Moran\n\n

\nI jus t took a DNA test\, and it turns out my data model is 100% wrong. This ses sion will focus on how to correctly data model for Apache Cassandra and No SQL databases. Topics will include: - A brief comparison of relational dat abases and NoSQL databases - The benefits of Apache Cassandra - Transition ing a relational data model to a Cassandra data model - Common issues that can be solved with a good data model This session is intended for folks n ew to Cassandra/NoSQL or folks transitioning from operations to a more dat a engineering and cloud-focused role.\n

\n\n

\nAmanda has been an committer and PMC member for Apache Trafodion since 2015. She was previou sly a Developer Advocate with DataStax where she spent many\, many hours h elping users get better with Apache Cassandra.\n

CATEGORIES:Cassandra URL;VALUE=URI:https://apachecon.com/acah2020/tracks/cassandra.html#R1935 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T0930@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T093000Z DTEND:20200929T101000Z SUMMARY:Apache Software Foundation and The Apache Way (in Hindi language) [ALC Indore] LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Swapnil M Mane\n\n

\nThis talk will be part of Track prepared by ALC Indore\, and *language for the talk will be Hindi*. In this session\, will speak about Apache Software Fo undation\, and about the Apache Way. # Apache Software Foundation -- Histo ry -- Projects -- How Apache project works? -- Apache Project Ecosystem # The Apache Way -- Community - over code -- Merit - recognizing your work - - Communication - how communities communicate -- Open Development -- Decis ion Making - Consensus \n

\n\n

\nAn open-source enthusiast\, and promoter. -- Apache Software Foundation Member -- Apache Central Services / Editorial Member -- Founder & Chair\, Apache Local Community (ALC) -- PM C Member\, Apache Community Development\, OFBiz\, Roller -- Founder Vue.js Indore community Skilled in E-commerce\, Order Management System\, Omni-C hannel\, and PWA strategy. Strong information technology professional with the intensive experience of building enterprise-grade applications. \n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T0930 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1010@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T101000Z DTEND:20200929T105000Z SUMMARY:InnerSource updates in China LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Jerry Tan\n\n

\nThere are more and more projects donated to Apache foundation from China in recent y ears. So more engineers are familiar with Apache Way. InnerSource is adopt ing Apache Way with an organization. And then More companies are beginning their InnerSource Journey. In this talk\, I will give a brief update of I nnerSource adoption in China\, including some company's practices. Some ar e using it to build engineer culture\, some are using it as a tool to remo ve duplicate wheels. InnerSource is a long journey\, but I am happy to see that Chinese companies are more willing to embrace Open Source more deepl y.\n

\n\n

\nCommitter of apache.org\, mozilla.org\, gnome.org PPM C of apache brpc incubating project\, InnerSourceCommon Foundation member\ , InnerSource advocator in China\, more than 20 years of Open Source Exper ience \n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1010 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1050@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T105000Z DTEND:20200929T113000Z SUMMARY:Apache Pulsar: a borderless community LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Jennifer Huang\n\n

\nApach e Pulsar is an open-source distributed pub-sub messaging system originally created at Yahoo and now part of the Apache Software Foundation(ASF). It is a multi-tenant\, high-performance solution for server-to-server messagi ng. After graduation from ASF\, the community grows bigger and stronger\, with more and more users and contributors. This presentation shares how Ap ache Pulsar develops a borderless community in a short period.\n

\n\n

\nJennifer Huang is an Apache Pulsar committer and a senior technical writer at StreamNative. She contributes to Apache Pulsar documentation an d community development proactively. She is dedicated to growing the Apach e Pulsar community globally. \n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1050 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:The Apache Way LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Kevin A. McGrail\n\n

\nThe Apache Way is how the Apache Software Foundation works. It's a collection of tribal knowledge and stories that loosely define how we work. Not all of it is intuitive but it does work. In 21 years we have changed the way c omputing around the world happens through our mission to provide open sour ce software to the world and doing so at no charge! Want to work more effe ctively with the foundation? Want to model our leadership? Want to bring a new project under our umbrella? Come learn more about the Apache Way! Kev in A. McGrail has served in a number of roles at the foundation as member\ , as a chairperson\, in the incubator\, as a mentor\, in the treasury and in fundraising. He will talk about his experience and some of the mistakes he's made too.\n

\n\n

\nKevin A. McGrail Director of Business Gr owth\, InfraShield https://www.linkedin.com/in/kmcgrail\, kmcgrail@infrash ield.com Kevin A. McGrail\, aka KAM\, is Director of Business Growth @ Inf raShield.com doing cyberphysical security for critical infrastructure. Kev in loves Open Source Software and is a member of the Apache Software Found ation. He is a cyber security and privacy expert\, and his research protec ts millions of Internet users every day. He is an advisor for SecurityUniv ersity.edu & Virtru.com as well as a Director at the Dysautonomia Support Network and The McGrail Foundation. His latest honor is becoming a member of the U.S. Marine Corps Cyber Auxiliary. Kevin has spoken all over the Un ited States and worldwide in Canada\, Germany\, Belgium\, Sweden & China o n Open Source Software\, the Cloud & Cybersecurity. \n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Open Source changes the world! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBertrand Delacretaz\n\n\n< p>\nIn a world that's increasingly digital\, Open Source software is every where: in your phone\, your elevator\, your car\, behind your bank account ...more than ever\, Open Source is at the heart of our world. Beyond these very concrete contributions to our society's well being\, Open Source com munities have also helped design innovative collaboration techniques\, esp ecially around remote and distributed work. Often running without a formal boss and without a formal schedule\, Open Source communities consistently produce software that's of great quality\, sometimes world-changing. COVI D-19 has prompted many companies and organizations to speed up their trans ition to digital transformation and distributed collaboration. This talk w ill show what Open Source communities can bring to this new world\, based on a number of concrete example where simple tools and techniques make all the difference in terms of digital and remote collaboration.\n

\n\n

\nBertrand Delacretaz works as a Principal Scientist for Adobe in Base l\, Switzerland. He's involved in software design and development for Adob e Experience Cloud products\, which use many open source modules\, mostly from Apache projects to which his teams contribute extensively. Bertrand i s currently (2020-2021) on his eleventh term as a member of the Apache Sof tware Foundation's Board of Directors and has been active in the Foundatio n for about 20 years.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1735@apachecon.com SEQUENCE:99 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Teaching Open Source LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDaniel Ruggeri\n\n

\nHow did you learn about Open Source? Did you learn from a mentor? Did you lea rn from a colleague? Did you learn through hard fought experience? For a l ot of us\, we learned the hard way. But... what if all of the concepts\, t he tools\, the licensing\, the methods\, and the terms were gathered into a course? Come join our presenter as he walks through his motivations and experience designing and teaching a college-level course about Open Source . We will discuss how to get started\, what the curriculum includes\, how you could deliver such a course\, and other tips and tricks.\n

\n\n

< em>\nDaniel is Vice President of Middleware at Mastercard and an Open Sour ce evangelist. Responsible for setting the direction of Mastercard regardi ng the Web and Cloud space\, he spends his days and nights playing with in frastructure and the code that powers it both inside the firewall and outs ide. He is a member of the Apache Software Foundation and has contributed code to Open Source projects from simple pet projects to widely utilized s ervers. As a lover of Open Source\, he even taught a course about Open Sou rce Software Development (and will share the curriculum with you!). He has spoken at several conferences about expanding Open Source in enterprises\ , introducing Open Source\, and growing understanding of Open Source in ed ucation.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:From no open source experience to Apache Member and PMC of Commons LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRob Tompkins\n\n

\nBetwe en 2016 and 2019 I went from having not made any Apache contributions to b eing on the PMC of Apache Commons. I will tell my story here and give insi ghts about how the Apache Way works. This talk has been previously given a t the DC Apache Roadshow with a positive response.\n

\n\n

\nI am a software developer and mathematician from Richmond\, Virginia who happen s to be an Apache Member and am on the PMC of Apache Commons. I also parti cularly enjoy outdoor adventure sports for those interested.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-community-T1935@apachecon.com SEQUENCE:0 DTSTAMP:20200928T141406Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:The State of D&I at the ASF LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAnita Sarma\, \nDaniel Izquierdo\,\ nGriselda Cuevas\,\nMariam Guizani\n\n

\nIn this talk we'll tal k about the research efforts the D&I committee has been working on for the past year. We'll talk with our researchers and will deep dive into the in sights and results we obtained from the three phases of our work: The ASF Community Survey\, the D&I research interviews and our quantitative analys is with Bitergia's technology. We'll also have an opportunity to ask quest ions to the researchers and the team behind this effort. \n

\n\n

\nAnita Sarma
\nAnita Sarma is an Associate Professor at Oregon State University. Before this she was an Assistant Professor at University of N ebraska\, Lincoln\; a post-doctoral scholar at Carnegie Mellon University\ , and a doctoral student at University of California\, Irvine. Through thi s journey her passion has been on helping humans make better software and work together. A primary focus of her research is in facilitating onboardi ng of newcomers and increasing diversity in open source projects. Overall\ , Dr. Sarma’s research has resulted in more than 100 peer-reviewed publica tions an several best paper records and the NSF CAREER award. Her work has been regularly funded through the National Science Foundation and Airforc e (AFOSR).
\n\nDaniel Izquierdo
\nDaniel Izquierdo Cortazar is a researcher and one of the founders of Bitergia\, a company that provides software analytics for open source ecosystems. Currently the chief data o fficer at Bitergia\, he is focused on the quality of the data\, research o f new metrics\, analysis\, and studies of interest for Bitergia customers via data mining and processing. Daniel holds a PhD in free software engine ering from the Universidad Rey Juan Carlos in Madrid\, where he focused on the analysis of buggy developers activity patterns in the Mozilla communi ty.
\nDaniel is an active member of the CHAOSS community at the D&I a nd GrimoireLab working groups as well as an active member of the InnerSour ce Commons.\n\nGriselda Cuevas
\n\nGriselda is the V.P. of D&I at the ASF and also a product manager in Google Cloud. She has 13 years of exper ience in a variety of industries\, from oil and gas to cloud computing. Sh e has a Masters in Operation Research and Data Science from UC Berkeley an d is passionate about data engineering\, open source technology\, informat ion architecture\, diversity and inclusion in tech & Italian wines.\nIn he r spare time she reads and works on diversity and inclusion topics\, speci ally around building frameworks that enable participation of under represe nted groups in tech.\n \nMariam Guizani\nMariam Guizani is a PhD student i n Computer Science at Oregon State University. Her research area is in Hum an-Computer Interaction and Software Engineering. She studies inclusivity in open source environments with a focus on supporting cognitive diversity from the tool perspective. She holds a Master of Science in Computer Scie nce from Oregon State University and was a recipient of the Fulbright scho larship in 2016.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W0900@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:building one active internal opensource community is crucial for I nnerSource LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Jerry Tan\n\n

\nInnerSourc e is the use of Apache Way within an organization. it needs both high-leve l support and internal engineers community support. Sometimes\, high-level support is easy to get and implemented\, but internal engineers' communit y support is much hard\, it means culture shift. So building one active in ternal open source community is very important. As OSPO of Baidu\, I adopt InnerSource inside Baidu for more than 4 years. I will talk about how I b uild the internal community in my company\, including how I plan\, how I i mplement it. I need to attract the target people\, set small tasks to let them participate and contribute\, and incentivize them. InnerSource is a l ong journey\, but it is worth the investment. \n

\n\n

\nCommitter of apache.org\, mozilla.org\, gnome.org PPMC of apache brpc incubating pr oject\, InnerSourceCommon Foundation member\, InnerSource advocator in Chi na\, more than 20 years of Open Source Experience\n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W0900 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W0940@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T094000Z DTEND:20200930T102000Z SUMMARY:Apache Local Community (in Hindi language) [ALC Indore] LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Priya Sharma\n\n

\nThis ta lk will be part of Track prepared by ALC Indore\, and *language for the ta lk will be Hindi* Apache Local Community (ALC) is an initiative by the Apa che Community Development project. ALC comprises local groups of Apache (O pen Source) enthusiasts\, called an 'ALC Chapter'. For details please refe r https://s.apache.org/alc The talk will include the details on introducti on\, Present State and next plans of ALC ## Introduction About ALC ALC Rol es and Responsibilities Benefits of ALC How to apply to set up ALC Chapter Code of conduct ALC Resources Addition information Contact ALC ## Present State Current ALC Chapters (Indore\, Beijing\, Warsaw\, Budapest\, and ot hers..) Activities and health of these chapters ## Beyond (Next Steps) Est ablishing new ALCs and future roadmap. How to participate in this initiati ve. More details on ALC can be found at -- https://s.apache.org/alc The fo llowing will be the take away from the session: -- What is ALC and how to participate in this initiative.\n

\n\n

\nCore member\, Apache Loc al Community Indore Chapter Contributor to Apache Projects since 2017\, ma jorly contributed in OFBiz\, and Community Development project. \n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W0940 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Apache Local Community (ALC): Present & Beyond LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Swapnil M Mane\n\n

\nApach e Local Community (ALC) is an initiative by the Apache Community Developme nt project. ALC comprises local groups of Apache (Open Source) enthusiasts \, called an 'ALC Chapter'. For details please refer https://s.apache.org/ alc The session will be majorly on two topics: #1.) How the Apache Softwar e Foundation provides the opportunity to flourish your idea. I shared the initial ALC idea to the community around mid-2019\, from there with the gr eat inputs from the community and mentors\, we have given the shape to the idea. It is a great example\, how the community can help you to transform and enhance your idea to match global standards. #2.) Introduction\, Pres ent State and next plans of ALC ## 2.1 Introduction About ALC ALC Roles an d Responsibilities Benefits of ALC How to apply to set up ALC Chapter Code of conduct ALC Resources Addition information Contact ALC ##2.2) Present State Current ALC Chapters (Indore\, Beijing\, Warsaw\, Budapest\, and oth ers..) Activities and health of these chapters ##2.3) Beyond (Next Steps) Establishing new ALCs and future roadmap. How to participate in this initi ative. More details on ALC can be found at -- https://s.apache.org/alc -- https://s.apache.org/alc-code-of-conduct -- https://s.apache.org/alc-guide lines -- https://s.apache.org/alc-chapters -- https://s.apache.org/alc-rep orts -- https://s.apache.org/establish-alc-chapter The following will be t he take away from the session: -- How community engagement can help in imp rovising and implementing your idea. -- What is ALC and how to participate in this initiative.\n

\n\n

\nAn open-source enthusiast\, and pro moter. -- Apache Software Foundation Member -- Apache Central Services / E ditorial Member -- Founder & Chair\, Apache Local Community (ALC) -- PMC M ember\, Apache Community Development\, OFBiz\, Roller -- Founder Vue.js In dore community Skilled in E-commerce\, Order Management System\, Omni-Chan nel\, and PWA strategy. Strong information technology professional with th e intensive experience of building enterprise-grade applications.\n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Growing with the Open-Source Community LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTomasz Urbaszek\n\n\n

\n During this talk\, I want to share lessons I've learned as a young enginee r contributing to an open-source project. Those include demystifying the s tereotype of OSS contributors\, \"community over code\" approach as well a s understanding the life cycle and funding of projects. But the most impor tant lesson is why young people should join open source communities early in their careers. They can gain tremendous experience which is quite often out of their reach when working on commercial projects. Also encouraging young people to join OSS project allow us as communities to validate our c ontribution guides and check if we create a really welcoming environment.\ n\n

\nTomek is a software engineer at Polidea and Apache Airflow com mitter. He is an open-source enthusiast and chapter lead of ALC Warsaw. Bo ok and philosophy lover with a big interest in financial markets. Tomek is a maths graduate from Warsaw University of Technology. \n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W1735@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Serve\, Lead\, Succeed the Open (mindful) Way to Prevent/Reverse B urnout in Boardrooms\, LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPrashant V. Joshi\n\n

\n Abstract: Open Source SW (OSS) community has been brilliant in bringing th e leadership out of everyone to innovate\, contribute and transform. Covid -19\, has brought one more challenge to the OSS community.This unique expe riential talk inspires and opens minds of new and seasoned OSS community m embers at large to become successful servant-leaders through self-care cop ing mechanisms to prevent and alleviate burnout in boardrooms\, classrooms and home-rooms in the midst of this pandemic and beyond. Yes you can! === Description: This talk engages the audience with simple\, practical\, sci entific\, rational and original ideas. It uses open and mindfulness princi ples so that every mind opens up (body too!)\, and gets inspired towards s elf-transformation. Whether you are a seasoned OSS guru or a novice/curiou s aspirant\, this talk is for you. Why? A closed mind is a dangerous thing that creates toxic leadership for oneself and others. “Misery loves compa ny” is a saying we love to use. Data shows that Rudeness costs millions to companies. Burnout is officially a disease according to World Health Orga nization. Depression costs lives. In a 2019 World Happiness Report surveyi ng 156 countries\, Finland was #1 (home of some Open Source pioneers) whil e the US was #19. So what? Time to shift the paradigm. It is mid-2020 with a re-surging Covid-19 pandemic and it is about time to bring clarity to o ur vision (pun intended!) so “happiness loves company too” - becomes a new phrase to live by. “Rudeness is expensive\, civility is NOT” is another l ine to live by too. Well\, how\, you ask? Through a short presentation fil led with scientific data\, we will define the science of leadership\, outl ine unique attributes of servant leadership\, and give examples of the sam e. We will end with a mindful experiential component to have fun and begin the science of self-transformation. Yes\, we can transform the OSS commun ity together with better (open=mindful) leaders\, awesome innovation\, fun ding\, and quality of life for all. Thank you\n

\n\n

\nPrashant V . Joshi M.A. M. Phil\, E-RYT 500\, C-IAYT\, YACEP (public speaker\, publis hed author) is an electrical engineer and a computer scientist. He is an o utcome-focused management & technology executive\, open source evangelist/ alliances-builder\, educator\, master coach/therapist and a social entrepr eneur with over 30 years in the US with many global for-profit and non-pro fit initiatives. His mantra for success is Grow PBT (People\, Business\, T echnology). Presently he is an executive advisor for an early-stage startu p (Sukhi Inc) in 'culturally aware mental wellness'. He is also a Global A mbassador to a non-profit institution in India (one of the oldest Yoga The rapy Research/Rehab Center) for raising funds for expanding a rehab/resear ch center for cancer and other life-style diseases. Most recently he was t he Vice President of Global Alliances for MariaDB Foundation\, an open sou rce project founded by Monty Widenius (creator of MYSQL). Over the past 5 years Prashant has advised many early and mature startups with his big bra nd and PBT acumen. Prashant is a co-founder of Gurukul\, LLC\, A Science o f Living Institution serving global communities with evidence-based yoga/h olistic healing which is in its 20th year. In 2016 he co-founded Food Yogi ni for advocating eco-friendly foods and goods. He has brought unique lead ership coaching\, Yoga/wellness into boardrooms\, classrooms and home-room s over the past 25+ years in NYC\, NJ\, TX and globally. He is a published author and a motivational public speaker. He lives in Austin\, TX with hi s wife Manju\, daughters Veda and Illa. He loves sports and travel and one of his tag-lines is Billions Yet To Be Served... Education: EE w honors f rom Bombay University and Double Masters in Computer Science from CUNY Wel lness Certifications: E-RYT 500 (experienced registered Yoga teacher w ove r 10\,000 hours of teaching)\, C-IAYT (certified International Yoga Therap ist (w over 5\,000 hours of therapeutic practice)\, YACEP (Yoga Alliance C ontinuing Education Provider) \n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W1815@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Lightweight Open Source LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nIssac Goldstand\n\n

\nEv er think about contributing to the Open Source world\, but worried that - for some reason or another - it’s not up to par with playing with the “big boys and girls”? That’s BS! Come learn why *every* contribution in the Op en Source world can be helpful! As an active participant in the Open Sourc e community for over twenty years\, I am constantly running into situation s where prospective newcomers to the community feel daunted and undervalua ting themselves and/or their contributions. In this session I’ll talk abou t the importance of that exact sort of small contribution in some of the b iggest Open Source communities and projects out there. Because sometimes a sub-domain expert is *much* more vital than yet another domain expert.\n< /p>\n\n

\nIssac has been involved in the Web community for nearly 20 years. With a strong background in the Apache Web Server internals\, and optimizing web applications\, Issac continues to churn out highly optimize d web applications in a variety of languages and servers\, as well as ment oring teams of programmers to be as passionate about writing great softwar e as he is. Today Issac is married with four kids\, and is launching a sta rt-up to chase his long time dream: turning smart homes into a day-to-day \"taken for granted\" reality. In his spare time\, he still spends time vo lunteering in the tech community and mentoring hi-tech teams across a dive rse range of languages and disciplines.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:The Apache Way: Practical Open Source Project Management LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShane Curcuru\n\n

\nThe Apache Way is useful for organizations and individuals to be more effectiv e at working in distributed communities. Open source software does not nec essarily mean open development - and true community-led open development i s where the fun starts in working in FOSS! There are a lot more aspects to consider and areas to invest in as you move forward through the open sour ce journey. These are just the starting points to work on. A key reminder: open source works best when you're working with softare that you actually use. Taking the time to choose which teams or projects that you open up o r participate is well worth the investment to keep your team's efforts foc used. Come learn the behaviors you can use to succeed at Apache!\n

\n\n

\nShane is founder of Punderthings℠ LLC consultancy\, helping organ izations find better ways to engage with the critical open source projects that power modern technology and business. He blogs and tweets about open source governance and trademark issues\, and has spoken at major technolo gy conferences like ApacheCon\, OSCON\, All Things Open\, Community Leader ship Summit\, and Ignite. Shane is serving a seventh term as an elected Di rector of the ASF\, providing governance oversight\, community mentoring\, and fiscal review for all Apache projects. Previously\, shane served as V P Brand Management for the ASF for eight years\, and wrote the trademark a nd branding policies that cover all 200+ Apache® projects\, including assi sting projects with defining and policing their trademarks\, as well as ne goitating agreements with various software vendors using Apache software b rands. Otherwise\, Shane is: a father and husband\, a BMW driver and punny guy. Oh\, and we have cats. Follow @ShaneCurcuru and read about open sour ce communities and see his FOSS Foundation directory: http://ChooseAFounda tion.com/\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-community-W1935@apachecon.com SEQUENCE:0 DTSTAMP:20200929T192946Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:The Myth of Culture LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKen Coar\n\n

\nIn today' s increasingly connected world\, the word \"culture\" appears in communiti es a lot. Unfortunately\, it's not a single-value term. In many cases the founders of a culture are unaware of how their ideals have morphed\, and a cquired altered or additional definitions. In this talk I intend to descri be some of the factors that contribute to this sort of 'fuzzing' of consen sual understanding of the term. \n

\n\n

\nKen Coar\, an open sour cerer and opinionist\, has written code for 40+ years. He was one of the f ounders of The Apache Software Foundation\, served on its board of directo rs for years\, and was responsible for the ApacheCon conferences for sever al years as well. He also served on the board of the Open Source Initiativ e. Currently he prefers to write code in Ruby\, but has contributed to rub ygems\, CPAN\, PHP\, and Apache httpd.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-community-R1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:The economics of vendor neutrality and vendor domination LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Myrle Krantz\n\n

\nGame th eory applied to open source can be used to explain how participants profit from open source\, but existing models work from the assumption that all contributors receive the same benefit from the decisions made. They do not . For example: Some may benefit more when software is faster\, while other s benefit when it is more configurable. It is often necessary to choose be tween the two. What happens to the general social welfare captured and mad e available by an open source project when a narrow economic interest domi nates its decision making?\n

\n\n

\nMyrle Krantz is currently ser ving as the Treasurer for the Apache Software Foundation. She is a former board member for the Apache Software Foundation\, conference chair for Apa cheCon Europe in 2019\, a member of the Diversity and Inclusion Initiative \, and the Community Development Committee. She has served as VP for the c ore banking open source project Fineract. Myrle has her computer science d egree from Rice University in Houston\, and her MBA from the Rotterdam Sch ool of Management. Myrle is an American living in the Voreifel in Germany with her two daughters\, a husband\, and a hunting dog. She loves to read\ , and plays piano badly.\n\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-community-R1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Welcoming community strengthens the Apache Way LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJarek Potiuk\n\n\n

\nThi s talk is about making the Apache Airflow a more welcoming community\, by applying more of the principles that are the core of Apache Way. This is t he story of the changes that we implemented over the last few years as a g roup of committers and PMCs in Apache Airflow. When our team started to co ntribute to Apache Airflow\, we realized how hard it was to start contribu ting to the project at the very beginning. While we became few of the most active community members (committers and PMCs) of Apache Airflow\, we lea rned our ropes\, but we have not forgotten that others have similar proble ms and from the very beginning we started to work on making it easier to b ecome the member of the community on many levels. We started from improvin g the development environment\, going through documentation improvements\, implementing some best coding practices and CI automation around it\, and ending at mentoring and communication guidelines. We would like to share with the other Apache projects some tips and learning on what you can appl y to be a more welcoming project. This talk will be half-deep-down technic al and half-soft-skills\, talking about how both sides are needed in order to be more welcoming. There is a \"Success at Apache\" blog and Featherca st video about this subject - now is the time to tell some more details in a talk.\n

\n\n

\nAfter more than 20 years of career in IT from a junior programmer to CTO of 60+ company\, Jarek has chosen to continue hi s path as individual contributor. He got back from the half-technical\, ha lf-managerial path he has been following since\, and with the vast experie nce in both technologies and business side of IT\, as well as being organi zer of 500+ attendees IT conference\, Jarek's engineering skills are suppl emented by an understanding of people\, business\, customers and partners\ , and with the strong understanding that relationships with people are the key to success in either of the roles. Jarek worked in many roles and man y types of companies and he tried it all - from few people mobile payment startup\, robotics + AI startup where he was a robotics engineer\, working at software house as an engineer and Head of Technology\, building and le ading 60+ software house\, Centre of Expertise expert at one of the bigges t FMCG companies\, and being Tech Lead Manager at Google\, and currently - became one of the most active full-time committers and PMC members of one of the most popular Open-Source Workflow Orchestrator for Big Data - Apac he Airflow. Jarek is an experienced technical team leader. Loves leading a nd motivating professional teams of developers\, testers\, IT admins\, pro ject managers\, but only if he can actively participate in all of the thin gs his people do. Patient mentor. Focused on achieving realistic deliverab les with quality matching the expectations. Balancing well research and pr agmatic approach\, with a strong flair for innovations. Jarek likes to use his voice and for a good reason.\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-community-R1735@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:How to help companies be the best open source participants possibl e LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nStormy Peters\n\n

\nComp anies are participating more and more in open source. Projects that figure out how to work most effectively with companies will benefit the most whi le maintaining their autonomy. Come discuss and learn the best ways to inc lude companies in your open source software plans. Learn how to leverage t he resources companies can bring to a project while maintaining your proje ct's governance model. Learn how to keep companies in the loop and still h ear all the individual voices. Learn how to accept resources without sacri ficing autonomy. The speaker is experienced in both running Open Source Pr ograms Office for large companies as well as leading open source software non-profits. This unique perspective to both sides allows her to bring som e novel suggestions. Many of us have experience working with open source s oftware projects and companies. This presentation will be a mix of real ex amples and audience discussion.\n

\n\n

\nStormy Peters is Directo r of the Open Source Programs Office at Microsoft. She works with people a nd teams across Microsoft to help make sure Microsoft uses and contributes to open source software in a way that makes it possible for the world to achieve more through open source software. Stormy is passionate about open source software and educates companies and communities on how open source software is changing the software industry. She is a compelling speaker w ho engages her audiences during and after her presentations. She has given keynotes at 4\,000+ person events such as OSCON\, PyCon and LinuxConf Aus tralia as well as talks to small groups. You can find videos of her talks online. Before joining Microsoft\, Stormy held leadership positions in ope n source and developer roles at Red Hat where she was head of the Communit y Leads\, the Cloud Foundry Foundation where she was VP of Developer Relat ions and Mozilla where she led Developer Relations. Previously\, she serve d as executive director of the GNOME Foundation and at OpenLogic where she set up their OpenLogic Expert Community. Stormy graduated from Rice Unive rsity with a B.A. in Computer Science. \n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-community-R1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Who pays for open source foundations? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShane Curcuru\n\n

\nOpen source sustainability is more than just individuals figuring out how to m ake a living off of open source. Have you ever wondered who actually pays for open source? ### Abstract Open source sustainability is more than just individuals figuring out how to make a living off of open source. Have yo u ever wondered who actually pays for open source? Not just developers\, b ut the whole ecosystem around major open source projects\, either at a FOS S Foundation\, independent or an open core project at a company? The major software projects we all rely on are mostly hosted at Foundations like Ap ache\, Eclipse\, Linux\, or Software Freedom Conservancy. Those foundation s provide a wide variety of support to project communities\, including leg al and licensing assistance\, trademark management\, event support\, and m ore. As non-profits\, these foundations rely on donors and sponsors for al l of their work. So who pays for all of this critical support for open sou rce foundations? Come find out what companies are behind the popular open source foundations and major independent projects\, and who's actually pay ing for all of the other support work that's done to keep the servers runn ing\, press releases coming\, and license compliance work. Surprises are g uaranteed\; I know I was surprised when I realized how many different FOSS projects that Microsoft is an annual sponsor for\, and what projects a fe w other companies supported with their cash.\n

\n\n

\nShane is fo under of Punderthings℠ LLC consultancy\, helping organizations find better ways to engage with the critical open source projects that power modern t echnology and business. He blogs and tweets about open source governance a nd trademark issues\, and has spoken at major technology conferences like ApacheCon\, OSCON\, All Things Open\, Community Leadership Summit\, and Ig nite. Shane is serving a tenth term as an elected Director of the ASF\, pr oviding governance oversight\, community mentoring\, and fiscal review for all Apache projects. Previously\, shane served as VP Brand Management for the ASF for eight years\, and wrote the trademark and branding policies t hat cover all 200+ Apache® projects\, including assisting projects with de fining and policing their trademarks\, as well as negoitating agreements w ith various software vendors using Apache software brands. Otherwise\, Sha ne is: a father and husband\, a BMW driver and punny guy. Oh\, and we have cats. Follow @ShaneCurcuru and read about open source communities and see his FOSS Foundation directory at http://ChooseAFoundation.com/\n

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-community-R1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:A view from the ivory tower: Participating in Apache as a member o f academia LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Mior\n\n

\nAcade mics in an ivory tower conjures images of people toiling away nicely insul ated from many of the concerns of reality. While this has it's advantages\ , anyone who's tried to use a project written for a research paper under a deadline can attest that it doesn't always result in useful code. While c ompleting my PhD\, I found an Apache project that fit well with the work I was doing s I rolled up my sleeves to write some code to make it more use ful for solving my own problems. I've since had the opportunity to join th e project's PMC and now as a faculty member\, I continue to find value in encouraging my own students to contribute to Apache projects. I'll discuss how academics and Apache projects can find mutual benefit in close collab oration. \n

\n\n

\nMichael completed his Masters degree at the Un iversity of Toronto and received a PhD from the University of Waterloo. Wh ile completing his PhD\, he began contributing to the Apache Calcite proje ct and has since joined the Calcite PMC. He joined RIT as an Assistant Pro fessor in 2018. His research revolves around schema design and management and data integration for non-relational data. He continues to look for opp ortunity for himself and his students to contribute to Apache projects.\n< /em>

CATEGORIES:Community URL;VALUE=URI:https://apachecon.com/acah2020/tracks/community.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-content-T1615@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Making New Friends - Traffic Control and Varnish LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nEric Friedrich\n\n

\nTra ffic Control is a full featured CDN Control Plane built around Traffic Ser ver caches. This session describes integration of ATC’s Traffic Router and Traffic Manager with Varnish Cache. Varnish Cache uses a specialized conf iguration language (VCL) which is generated alongside Traffic Control conf iguration files.\n

\n\n

\nEric is currently a Content Distributio n Architect with Disney Streaming Services. He is also a PMC member and co mmitter of Apache Traffic Control.\n

CATEGORIES:Content Delivery URL;VALUE=URI:https://apachecon.com/acah2020/tracks/content.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-content-T1655@apachecon.com SEQUENCE:3 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Flexible Topologies: Scaling your CDN to N tiers LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nZach Hoffman\, Robert O Butts\, Jer emy Mitchell\n\n

\nUntil recently\, any CDN built using Apache Traffic Control would be limited to at most 2 tiers—an Edge Tier and a Mid Tier—which limits the CDN's ability to scale as needed. A recent project allows a CDN to be broken into Topologies\, each of which can span any num ber of tiers. This talk explores the changes to the project this initiativ e has involved\, the capabilities of Flexible Topologies\, and the steps i nvolved to adapt an existing CDN to use Flexible Topologies.\n

\n\n

< em>\nZach Hoffman lives in Denver\, Colorado and is a software engineer at Comcast. They spend their spare time contributing to online puzzle game c ommunities and playing the piano. When working on Apache Traffic Control\, they focus on its Go and Java components.\n

\n

\nRobert O B utts is a software engineer who works on Apache Traffic Control for the Co mcast CDN. Rob is a Principal Engineer at Comcast with a Masters in Comput er Science focusing on Parallel Processing. Rob has worked on nearly every component of the Apache Traffic Control CDN. He is the primary author of Traffic Monitor\, was the initial primary author of the Golang Traffic Ops \, and wrote the Grove HTTP Caching Proxy. He is currently working on exte nding Apache Traffic Server for Traffic Control's needs.\n

\n

\nJeremy has been an Apache Traffic Control contributor for over 5 years with a primary focus on the Traffic Portal (UI) and Traffic Ops API compo nents. During that time\, he has witnessed exponential growth of the Comca st CDN enabled by the power\, flexibility and reliability of ATC.\n CATEGORIES:Content Delivery URL;VALUE=URI:https://apachecon.com/acah2020/tracks/content.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-content-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Preview: Parent Selection Strategy Plugins\, How They Work\, and W hat They Mean for Apache Traffic Control LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRobert O Butts\n\n

\nPar ent Selection Strategies are an upcoming feature of Apache Traffic Server. We will discuss how they work\, and why they're valuable to Apache Traffi c Control. We will also preview Parent Selection Strategy Plugins\, a feat ure currently being developed\, what they may look like\, how a plugin may be written by ATS users and ATC administrators\, and the additional benef its Strategy Plugins offer to Apache Traffic Control deployments.\n

\n\ n

\nRobert O Butts is a software engineer who works on Apache Traffi c Control for the Comcast CDN. Rob is a Principal Engineer at Comcast with a Masters in Computer Science focusing on Parallel Processing. Rob has wo rked on nearly every component of the Apache Traffic Control CDN. He is th e primary author of Traffic Monitor\, was the initial primary author of th e Golang Traffic Ops\, and wrote the Grove HTTP Caching Proxy. He is curre ntly working on extending Apache Traffic Server for Traffic Control's need s.\n

CATEGORIES:Content Delivery URL;VALUE=URI:https://apachecon.com/acah2020/tracks/content.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-content-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Traffic Ops API Design LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBrennan Fieck\n\n

\nFor the past few months\, the Traffic Ops working group has been iterating on a design document for the Traffic Ops API. Some pieces of it have already been incorporated into the existing API\, others are still a work in progr ess. This talk will be an overview of the design in progress\, motivations and considerations\, and lessons learned.\n

\n\n

\nBrennan is a software engineer on the CDN team at Comcast. Brennan is an Apache Traffic Control committer and one of the leads of the Traffic Ops Working group.\ n

CATEGORIES:Content Delivery URL;VALUE=URI:https://apachecon.com/acah2020/tracks/content.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-content-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Uplink redundancy for Apache Traffic Control CDN Caches LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSergey Dremin\n\n

\nApac he Traffic Control CDN Caches at Comcast have been configured with a simpl e LAG with LACP connection to a single uplink router with a single IP. Tha t created maintance costs for the CDN caused by router maintanence\, and i mpacted overall reliability during router outages. To solve these problems an update to ATC now enables configuring connections to multiple uplink r outers. Virtual IPs can now be assigned to the cache and advertised to the rest of the network via BGP peering allowing further flexibility with con tent routing.\n

\n\n

\nSergey is a Sr Engineer on the CDN team at Comcast.\n

CATEGORIES:Content Delivery URL;VALUE=URI:https://apachecon.com/acah2020/tracks/content.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-content-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Extending Automation towards Self-Service CDNs LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJonathan Gray\n\n

\nApac he Traffic Control is a set of applications designed to complement Apache Traffic Server to comprise a Content Delivery Network. Currently the creat ion of production-like CDN environments is a complex process. I will be de monstrating how I’ve been able to augment existing OSS Ansible automation to produce disposable test CDN environments.\n

\n\n

\nJonathan Gr ay has been with Comcast on the Content Delivery Network team for approach ing 3 years focusing on Operations and Automation. Prior to that he's serv ed as a software developer\, integrator\, and devops lead for Milsoft Util ity Solutions for 7 years. He holds a Bachelor of Science Degree in Comput er Science from Abilene Christian University where he also served as an IT Systems Administrator\, Datacenter Administrator\, and Virtualization Adm inistrator for over 3 years.\n

CATEGORIES:Content Delivery URL;VALUE=URI:https://apachecon.com/acah2020/tracks/content.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Apache cTAKES: First Principles and Customization LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSean Finan\n\n

\nBuilt u sing Apache UIMA\, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) is a modular and extensible tool for Natural Language Proc essing. This is a quick start tutorial on adding custom elements to cTAKES . We illustrate creating simple classes to input\, process and output data . This involves a concise overview of Apache uimaFIT and the cTAKES type s ystem\, as well as building a UIMA pipeline using piper files.\n

\n\n

\nSean Finan is a software developer in the Natural Language Processi ng lab at Boston Children's Hospital. He has worked with Apache cTAKES for the past 8 years\, contributing code and supporting the community.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Integration UIMA components into cTAKES LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSiamak Barzegar\n\n

\nAp ache cTAKES (clinical Text Analysis and Knowledge Extraction System) is an open-source Natural Language Processing system for extraction of informat ion from Electronic Health Records (EHR). cTAKES consists of a number of c omponents that work just with English documents. We integrated two importa nt tools (HeidelTime and FreeLing) into cTAKES that provide language analy sis functionalities (Temponym Tagging\, Morphological Analysis\, Named Ent ity Detection\, PoS-Tagging\, Parsing\, Word Sense Disambiguation\, Semant ic Role Labelling\, so forth) for a variety of languages. Also\, we adapte d HeidelTime’s grammar and FreeLing to the Medical domain in Spanish. Due to having different type systems in components of cTAKES and HeidelTime an d FreeLing\, we had interoperability challenges that were solved by adapti ng the native type system of cTAKES for HeidelTime and FreeLing’s Wrapper. \n

\n\n

\nSiamak Barzegar is a Senior Research Engineer at Biomed ical Text Mining Unit at Barcelona Supercomputing Center in Spain. He won the Science Foundation Ireland (SFI) research scholarship and received his PhD degree from the National University of Ireland\, Galway in December 2 018. The main area of his work/research is focusing on Natural Language Pr ocessing\, Distributional Semantics\, Word Embeddings\, Deep Learning\, Kn owledge Extraction on Multilingual & Specific Domains.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Secret Engines of Apache cTAKES LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSean Finan\n\n

\nThe Apa che clinical Text Analysis and Knowledge Extraction System (cTAKES) defaul t pipeline is a standard in the natural language processing clinical resea rch community. What is past that standard? While the default clinical pipe line uses almost 20 analysis engines\, there are dozens more in various cT AKES modules. We present and discuss the top 5 annotation engines you neve r knew you had.\n

\n\n

\nSean Finan is a software developer in th e Natural Language Processing lab at Boston Children's Hospital. He has wo rked with Apache cTAKES for the past 8 years\, contributing code and suppo rting the community.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Advanced Dictionary use in Apache cTAKES LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSean Finan\, Jeff Miller\n \n

\nNamed Entity Recognition is at the core of all complete natural lan guage processing tools. Out of the box clinical Text Analysis and Knowledg e Extraction System (cTAKES) uses a dictionary containing part of the Unif ied Medical Language System (UMLS) that covers most common clinical terms. But it also comes with a custom dictionary creator. If you think that you r clinical research is directed\, then you should probably have a directed dictionary. UMLS subsets\, non-english dictionaries and novel custom dict ionaries have all been successfully used with cTAKES. This is an overview of cTAKES named entity recognition with the essential what\, why and how o f custom dictionaries as the centerpiece. Also discussed will be configura tion to use discontiguous spans and subsumption of short terms.\n

\n\n< p>\nSean Finan:
\nSean Finan is a software developer in the Natur al Language Processing lab at Boston Children's Hospital. He has worked wi th Apache cTAKES for the past 8 years\, contributing code and supporting t he community.
\nJeff Miller:
\nJeff Miller leads a team of data scientists at the Children's Hospital of Philadelphia (CHOP). His work foc uses on developing tools to help researchers analyze clinical data. Jeff h olds a master's degree in applied statistics from Penn State University.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:REST Support for Apache cTAKES LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGandhirajan N\, Sean Finan\n\n

\nApache cTAKES™ is a natural language processing system for the ex traction of information from electronic medical record clinical free-text. It's predominantly a desktop-based application. This session will talk ab out enabling REST support in cTAKES. We will be setting up UMLS knowledge sources in MySQL DB using scripts generated by cTAKES Dictionary Creator G UI which in turn uses MetamorphoSys UMLS installation wizard. We will depl oy the cTAKES web REST module in tomcat and the application will use the c TAKES engine to perform analysis of the payload passed via REST call again st the MySQL DB source and returns the analysis findings as JSON. We will also have a quick demo of the steps mentioned above. This will help health care industry to perform NLP analysis using cTAKES engine with just a REST endpoint.\n

\n\n

\nGandhirajan N:
\nSoftware developer with 15 years of experience in product design and development. Currently worki ng on developing cloud-native applications using Spring Boot and deploying the same in Azure. Apache committer in cTAKES and Cordova projects.
\nSean Finan:
\nSean Finan is a Software Developer in the CHIP-NLP gr oup\, contributing his experience to their ongoing projects that utilize a nd help expand the capabilities of Natural Language Processing. Originally a Geophysicist and Materials Scientist\, Sean gained his interest in soft ware development while creating computer simulations as analogues of physi cal processes studied in his laboratory research. After leaving academia a nd a year of employment at the Mayo Clinic\, Sean moved to Houston to work eleven years with Landmark Graphics\, the leading provider of scientific software for the energy industry. PMC and committer in Apache cTAKES proje ct.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:SpaCTeS: Extraction of Information on Diagnosis of Stroke from Ele ctronic Health Reports LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSiamak Barzegar\n\n

\nMo st of the relevant data produced on stroke clinical settings consist of un structured data (clinical narrative texts in Electronic Health Records (EH R). We tested new TM techniques to assist in the process of extracting rel evant information from hospital discharge reports of patients diagnosed wi th a stroke (2016 to 2017). We developed a TM pipeline structured into ite rative phases to gradually improve the quality of transforming narrative d ischarge reports into structured clinical data representations and generat ing good practice recommendations. The initial system was developed using Apache cTAKES\, a natural language processing for information extraction f rom the EHR system initially developed by the Mayo Clinic. The main challe nge was the heterogeneity of source data (3000 documents in Spanish and Ca talan from 28 different hospitals). We developed an analysis tool to test the quality of texts by identifying missing information and non-standard u sage of notations and vocabularies. The system also produced a normalized version of the texts. These results allowed us a detailed analysis of the stroke narrative records and the identification of aspects such as heterog eneity (and its problems) and degree of standardization\, all of which are critical to enabling better exploitation of the information contained in EHR by TM approaches.\n

\n\n

\nSiamak Barzegar is a Senior Resear ch Engineer at Biomedical Text Mining Unit at Barcelona Supercomputing Cen ter in Spain. He won the Science Foundation Ireland (SFI) research scholar ship and received his PhD degree from the National University of Ireland\, Galway in December 2018. The main area of his work/research is focusing o n Natural Language Processing\, Distributional Semantics\, Word Embeddings \, Deep Learning\, Knowledge Extraction on Multilingual & Specific Domains .\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Customize cTAKES for Automated Adverse Drug Event Surveillance in Pediatric Pulmonary Hypertension LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nChen Lin\n\n

\nBased on the Apache clinical Text Analysis Knowledge Extraction System (cTAKES)\, a n open-source NLP system\, we built a customized pipeline and processed 14 9\,038 notes for 984 pediatric Pulmonary Hypertension (PH) patients for de tecting textual mentions and signs/symptoms that may represent adverse dru g events (ADE). Our pipeline featured a customized dictionary for interest ed term mentions and emphasized term negation\, temporality of events\, pr oximity among mentions\, for a refined detection for co-occurrence of medi cations and potential drug effects. Analysis showed our automatic ADE dete ction system identified up to 7-fold higher ADE rates than those ascertain ed from diagnostic codes.\n

\n\n

\nChen is an Applications Develo pment Specialist in the Children’s Hospital Informatics Program-Natural La nguage Processing (CHIP-NLP) group. Chen is actively incorporating statist ical and machine learning technologies into advanced NLP tasks being inves tigated here at CHIP-NLP. Topics include automatic feature selection\, cor eference resolution\, disease activity classification based on clinical na rratives\, etc. Chen has worked on several projects including the developm ent of a novel complementary mining process that made use of unused featur es by a priori defined phenotypes\; he authored interactive phenotype-mini ng and visualizing software\; in addition Chen has research experience in deriving human cancer gene interaction networks based on genome-wide survi val analysis.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-R1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Extracting Patient Narrative from Clinical Notes : Implementing Ap ache Ctakes at scale using Apache Spark LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDebdipto Misra\n\n

\nPat ient notes not only document patient history and clinical conditions but a re rich in contextual data and are usually more reliable sources of medica l information compared to discrete values in the Electronic Health Record (EHR). For a medium-sized integrated Health System like Geisinger this amo unts to approximately fifty thousand notes each day. For information extra ction on retrospective data\, the volume can run into millions of notes de pending on the selection criteria. This talk describes the journey taken b y the Data Science Team at Geisinger to implement a distributed pipeline w hich uses Apache Ctakes as the Natural Language Processing (NLP) Engine to annotate notes across the entire spectrum of patient care. From re-writin g certain components in the Ctakes engine to architecting data store and p ipeline optimization for a better throughput\, this talk delves into vario us technical difficulties faced while aspiring to truly do NLP at scale on clinical notes. Towards the end\, the talk also demonstrates few usecases and how using Ctakes has helped clinicians and stakeholders to extract pa tient narratives from patient notes using Apache Solr and Banana.\n

\n\ n

\nDebdipto Misra is a Data Scientist with Geisinger Health. Previo usly\, he worked with AOL Inc. as a Platform Engineer in Audience Analytic s and with EMC Corp. as a Systems Engineer. He has worked in the Data Mini ng and Analytics space for over half a decade. He won a fellowship and pre sented the “Evolution of Prosthetics using Pattern Recognition on Ultrasou nd Signals” at the 2014 IEEE Big Data Conference in Washington\, DC. He ha s also published at multiple journals and presented at healthcare conferen ces like HIMSS. Currently\,his main focus is on building capacity planning tools for healthcare organizations for bed-supply demand using various de ep learning approaches and integrating it with patient notes.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-R1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T213949Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Fault-Tolerant\, Distributed\, and Scalable Natural Language Proce ssing with cTAKES LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJeritt Thayer\, Jeffrey Miller\n\n

\nElectronic health records contain a substantial amount of cli nical information as unstructured free text. This information has the pote ntial to enhance clinical decision making as well as provide insight for s econdary health related research. Apache Clinical Text Analysis and Knowle dge Extraction System (cTAKES) is a health specific natural language proce ssing (NLP) system that has demonstrated success in the health care indust ry. However\, analyzing large sets of notes with cTAKES can take months or even years to complete. By combining cTAKES with Apache Spark\, we develo ped a fault-tolerant and scalable NLP pipeline that respects the single th readed limitation inherent in cTAKES pipelines. It is capable of processin g millions of clinical notes in minutes on a large computing cluster. We h ave also configured the pipeline to make it easy to adjust common settings like changing negation detection algorithms and toggling whether or not t o detect entities over discontinuous spans. At the completion of this sess ion\, you will have a practical example of processing large volumes of uns tructured text using cTAKES and be able to identify the benefits of using different Apache distributed computing frameworks such as Spark and Beam.\ n

\n\n

\nJeritt Thayer
\nJeritt Thayer is a software enginee r at Children's Hospital of Philadelphia. His work focuses on designing\, developing\, and evaluating novel systems to support patient engagement\, medical decision making\, and care delivery. Prior to his career in softwa re\, Jeritt was a professional soccer player. Jeritt is passionate about d eveloping applications that support asynchronous and non-colocated communi cation to improve provider coordination and patient outcomes.
\nJeff Miller:
\nJeff Miller leads a team of data scientists at the Children 's Hospital of Philadelphia (CHOP). His work focuses on developing tools t o help researchers analyze clinical data. Jeff holds a master's degree in applied statistics from Penn State University.\n\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-ctakes-R1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Apache cTAKES and Python\; Apache cTAKES High Throughput Orchestra tion LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDmitriy Dligach\, Sean Finan\, Pete r Abramowitsch\n\n

\n1. The rise of Natural Language Processing Machine Learning libraries in Python has created opportunities for the Ap ache clinical Text Analysis and Knowledge Extraction System (cTAKES). Ther e are also challenges in utilizing the Java-based cTAKES type system acros s platforms. 2. We have built a high throughput orchestration mechanism to process and publish millions of redacted and unredacted notes in a PHI-sa fe environment and to manage refreshes where notes can continually be re-r edacted\, or obsoleted. We have a high-urgency stream of Covid related not es that are on a weekly refresh basis.\n

\n\n

\nDmitriy Dligach:< br />\nThe overarching goal of Dr. Dligach's research is developing method s for automatic semantic analysis of texts. His work spans such areas of c omputer science as natural language processing\, machine learning\, and da ta mining. Most recently his research has focused on semantic analysis of clinical texts. He works both on method development and applications.
\nSean Finan:
\nSean Finan is a software developer in the Natural La nguage Processing lab at Boston Children's Hospital. He has worked with Ap ache cTAKES for the past 8 years\, contributing code and supporting the co mmunity.
\nPeter Abramowitsch:\nPeter Abramowitsch started using cTAK ES while working in the Hearst Health Innovation Lab. He is now an Archite ct and cTAKES Implementer in Bakar Computational Health Sciences Institute at the University of California\, San Francisco.\n

CATEGORIES:cTAKES URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ctakes.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-T1615@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:The present of Fineract - Panel discussion LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJavier Borkenztain\, Michael Vorbur ger\, Ed Cable\, James Dailey\n\n

\nIn this panel\, we will dis cuss how the community is working\, what are the current challenges and wh at are we seeing from our unique perspectives.\n

\n\n

\nJavier Bo rkenztain:
\nJavier is a serial entrepreneur with more than twenty ye ars of experience working with technology in several industries and more t han a decade in the financial industry. He is co-Founder and CEO of Fiter. Javier was involved with the Apache Fineract and Mifos X communities sinc e 2014\, and he had several roles within The Mifos Initiative. He was the Founder and CEO of the first Latin American startup granted with a banking grade license from a Central Bank. Javier holds a title of Industrial and Mechanical Engineer from the Universidad de la República del Uruguay\, MB A from the IEEM\, Universidad de Montevideo.
\nMichael Vorburger:
\nFather. EPFL alumni. Working on The Supercomputer for advertisement & more (at G)\; prev. at @RedHat. Also ScratchDay.ch\, Fineract.dev
\nE d Cable:
\nPioneer in catalyzing community growth and financial inclu sion innovation as the leader of the global open source Mifos community fo r the past decade. Perched at the compelling intersection of financial inc lusion and open source technology\, I'm well-versed in fintech from multip le dimensions:
\nJames Dailey:
\nJames Dailey is the Board Chair and founder of Mifos\, the open source community that created and then co ntributed the fineract code base. He works at the intersection of financia l inclusion and energy inclusion in the global south. A serial entrepreneu r\, James has created several social ventures and open source projects\, a imed at solving big hairy problems. He sits on the fineract PMC and helps with identifying gaps and strategies.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Leverage Fintech with Fineract LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJavier Borkenztain\n\n

\ nFintech is disrupting the financial industry\, as Open Source disrupted t he software industry. Now the two of them meet at Fineract. The Open Sourc e platform for financial disruption. In this presentation\, we will explor e how Fineract can be utilized to implement Fintech applications from Nige ria to Mexico.\n

\n\n

\nJavier is a serial entrepreneur with more than twenty years of experience working with technology in several indust ries\, and more than a decade in the financial industry. He is co-Founder and CEO of Fiter. Javier was involved with the Apache Fineract and Mifos X communities since 2014\, and he had several roles within The Mifos Initia tive. He was the Founder and CEO of the first Latin American startup grant ed with a banking grade license from a Central Bank. Javier holds a title of Industrial and Mechanical Engineer from the Universidad de la República del Uruguay\, MBA from the IEEM\, Universidad de Montevideo.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Fineract: Reinvigorating Community LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Vorburger\n\n

\n The Apache Fineract community has seen a marked uptake in new activity in PRs and on the dev mailing list. This talk will present some of the measur es that we have taken which enabled this. It will use the Apache Fineract project as an example\, but the shared lessons learnt will be generally ap plicable.\n

\n\n

\nMichael is a long time open sourcerer who over the years has been involved in too many projects to enumerate. He started in FLOSS one fateful night many moons ago through a friendly interaction with a maintainer on an IRC channel of what was then the Mifos project. Ev er since\, he has been actively supporting the humanitarian open source pl atform for financial inclusion that is now known as Apache Fineract in a v olunteer capacity. Currently employed by Google\, previously at Red Hat\, this talk is given in a purely personal capacity.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Open Source as a counterweight to PlatFins (facebook\, Google\, Te ncent\, Alibaba) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJames Dailey\n\n

\nThe p ublic square needs open source for payments\, banking\, identity and finan cial inclusion. Fortunately\, a number of projects\, some new\, some old a re there to provide a stack for payments and banking for everyone on the p lanet. We’ll survey some recent developments in Fineract\, Mifos Payment G ateway\, Mojaloop\, and related technologies around auth\, identity\, and put it together with announcements around open G2P\, open Banking\, CBDC\, and open ID. We'll get into tech enablers like biometrics on a secure dev ice. We’ll put together a stack of technology built on Fineract\, and demo nstrate what might be possible. From remittances to self-sovereign identit y\, we’ll explore how these new trends intersect with specific technologie s. Low cost\, cloud\, offline device enabled\, the future is open.\n

\n \n

\nJames Dailey is the Board Chair and founder of Mifos\, the open source community that created and then contributed the fineract code base . He works at the intersection of financial inclusion and energy inclusion in the global south. A serial entrepreneur\, James has created several so cial ventures and open source projects\, aimed at solving big hairy proble ms. He sits on the fineract PMC and helps with identifying gaps and strate gies.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200914T192219Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Open Banking: a Revolutionary Democratizing Force for Financial Se rvices Innovation LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMatt Millar\,\nAli Hussein Kassim\, \nVictor Romero\n\n

\nThis panel consisting of technologists an d practitioners from the Fineract ecosystem will look at how Open Banking is and will dramatically transform how financial services are delivered. T he panel will explore how Open Banking has already impacted sectors like t he UK and Europe\, the revolutionary potential it will have for the underb anked in emerging markets\, the ongoing status and roadmap of Open Banking APIs being implemented on top of Fineract\, as well trends and emerging A PI standards that will continue to unlock new innovation democratizing fin ancial services.\n

\n\n

\nMatt Millar:
\nMatt is a serial en trepreneur with experience in delivering mass consumer mobile solutions. A t Updraft he is co-founder and CTO\, Updraft helps get consumers out of de bt\, and achieve their financial goals. Prior to updraft Matt built the on line booking platform for Slick\, a startup founded by Brent Hoberman’s Fo under’s Factory and founded and built Tellybug\, which pioneered app votin g for TV shows including X Factor\, Britain’s Got Talent\, The Voice on pr imetime broadcasters including BBC\, ITV\, and broadcasters across Germany \, Denmark\, Poland\, Sweden\, Singapore\, Thailand\, Australia and more. A pioneer in the mobile industry Matt was building consumer applications o n mobile before App Stores existed\, delivering applications embedded in m obile phones produced by Nokia\, Samsung\, Ericsson and more.
\nAli H ussein Kassim:
\nAli is the CEO of Kipochi\, a Pan-African Fintech co mpany that enables the financial ecosystem to utilize digital technologies towards enhancing efficiency\, bring innovative financial solutions to th e unbanked across the continent and create awareness towards the transform ative nature of Financial Technologies. Ali is a Co-Founder and Partner at Demo Ventures. DEMO Ventures is an early stage\, smart capital fund\, cur rently raising its inaugural fund\, focused on digital innovation and digi tal transformation in selected sectors in Africa. DEMO leverages its propr ietary Pan African deal to catalyze investment into early stage\, high gro wth potential startups in Africa. Ali is a Global Board Advisor at the Mif os Initiative. As a true visionary in the fintech world\, he uses his expe rtise and critical insight into the sector to help shape the ongoing produ ct and community development strategy and advance sustainability endeavors for the Mifos Initiative.
\nVictor Romero:
\nVictor is a profes sional with more than 20 years of experience in the Information Technology sector\, throughout which he has consolidated knowledge and forged techni cal and administrative skills focused on providing solutions\, meeting nat ional and international quality standards\, focused mainly on the Financia l sector. He has participated in Development\, Operations\, Management and Control areas\, the experience in these areas gives him a broad vision to take strategic decisions that enhance the social and business objectives promoted by technology demanded by financial institutions\, always acting with a high sense of professional ethical values. He co-founder of Fintech eando where we have been working with the Mifos Initiative using Fineract 1.x/CN for providing digital solutions for the Mexican financial instituti on which serve the population at an enterprise level.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-T1935@apachecon.com SEQUENCE:3 DTSTAMP:20200810T143451Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Digital Field Applications: Exploring the Spectrum of Apps to Trul y Reach the Last Mile. LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAvik Ganguly\n\n

\nFrom loan origination to loan collections\, from e-commerce to remittances\, fr om savings to billpay\, there are a wide range of use cases and different types of field staff and agents that DFAs enable. The APIs in Fineract alr eady power a broad variety of digital field applications but there are man y more use cases we could enable. Avik Ganguly of Fynarfin will explore th e spectrum of digital field applications\, the requirements they entail an d the roadmap we can advance in Fineract. He’ll illustrate the breadth of the Fineract APIs by presenting a case study on the agent banking solution his team has built on top of Fineract.\n

\n\n

\nAvik Ganguly is the founder of Fynarfin\, a fintech company building scalable enterprise s olutions on top of Apache Fineract. Avik is a Fintech specialist and open source evangelist who picked up his trade while working with Mifos\, Confl ux and Novopay.
\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-W1615@apachecon.com SEQUENCE:4 DTSTAMP:20200810T213949Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Fineract in 2030 LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJavier Borkenztain\, Saransh Sharma \, David Yahalomi\, James Dailey\, Gabriele Columbro\n\n

\nIn t his panel\, we will discuss the future of our community and product.\n

\n\n

\nJavier Borkenztain:
\nJavier is a serial entrepreneur wi th more than twenty years of experience working with technology in several industries and more than a decade in the financial industry. He is co-Fou nder and CEO of Fiter. Javier was involved with the Apache Fineract and Mi fos X communities since 2014\, and he had several roles within The Mifos I nitiative. He was the Founder and CEO of the first Latin American startup granted with a banking grade license from a Central Bank. Javier holds a t itle of Industrial and Mechanical Engineer from the Universidad de la Repú blica del Uruguay\, MBA from the IEEM\, Universidad de Montevideo.
\n Saransh Sharma:
\nResearcher at Muellners
\nDavid Yahalomi
\nDavid is the co-founder of Hypercore a new startup that provides non-ban ks a SaaS platform for credit issuing\, and Articode\, a software developm ent company that specializes in financial and real-time GIS systems develo pment and deployment. David has worked for the first digital bank in Israe l - Pepper - as an R&D team leader and after seeing the legacy systems use d by the current banks\, has decided to join the Fineract community after falling for the idea of open-source core financial system.\nDavid and his team have developed Fineract as a service\, a free service that provides e asy provisioning of a Fineract tenant for developers and startups looking to build on or experiment with Fineract and Mifos X.
\nJames Dailey\nJames Dailey is the Board Chair and founder of Mifos\, the open sour ce community that created and then contributed the fineract code base. He works at the intersection of financial inclusion and energy inclusion in t he global south. A serial entrepreneur\, James has created several social ventures and open source projects\, aimed at solving big hairy problems. H e sits on the fineract PMC and helps with identifying gaps and strategies.
\nGabriele Columbro
\nGabriele is an open source executive and technologist at heart. He spent over 15 years building developer ecosystem s to deliver value through open source across Europe and the US. He thrive s on driving innovation both contributing to open source communities and j oining commercial open source ventures\, whether it’s for an early stage t ech startup\, a Fortune 500 firm or a non profit foundation. Previously Di rector of Product Management at Alfresco\, as Executive Director Gabriele grew the Fintech Open Source Foundation FINOS from the ground up\, with th e vision of creating a trusted arena for the global financial services ind ustry to innovate faster\, leveraging open source as a model of collaborat ion. Gabriele holds a Master in Computer Engineering\, is a Committer for the Apache Software Foundation and advises open source startups. He’s a pa ssionate soccer fan\, reggae music connoisseur and special needs dad and a dvocate wannabe.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:How to implement a digital bank with a social approach using open source LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRaúl Sibaja\, Karina Ortiz\n\n

\nLatin America has become a world benchmark where social mobility is hampered by public policies\, access to information and therefore to fi nancial services. The use of Open Source\, Fineract in this particular cas e\, allows to be the base of the ecosystem that is changing the difficulti es in opportunities for Public and Private Institutions that have a perspe ctive of social and financial inclusion. In the proposal we expose the cha llenges we face in sharing knowledge to the Fineract and Open Source commu nity.\n

\n\n

\nRaúl Sibaja:
\nDegree in Applied Mathematics and Computing. Implementation of Core Banking in financial institutions. F inancial education workshops for banks and educational institutions. Parti cipation in hackathons to provide innovative solutions to financial inclus ion through the use of artificial intelligence and personal assistants.
\nKarina Ortiz:
\nLicenciada en Matemáticas Aplicadas y Computació n. Desarrollo de aplicaciones móviles híbridas y páginas web. Experiencia en el sector fintech. Participación en diversos hackathons para brindar so luciones a la inclusión financiera.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Fintech\, the most disruptive technology of the Century. LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMaria Luisa Martinez\n\n

\nThe 4th Industrial Revolution\, technology has made us jump light-years in terms of well-being\, human capacity\, knowledge\, equality\, and soci al inclusion. The change has been so big\, so profound and so fast\, that not everybody has been available to experience it\, and not every industry could be adaptable and innovative enough to survive this change. The powe r of Technology used for good\, and all the benefits it can provide\, has not only changed our ways of living\, but also our ways of thinking\, brin ging financial inclusion (a key pillar in social inclusion) to places neve r think of thanks to mobile and electronic money. Technology and innovatio n applied to Finance with Financial Inclusion as its mission and vision\, took out of poverty millions of people\; giving them access not only to fi nancial services\, but also to education\, safety and equality. In additio n\, half of the world’s population is under 30: all background people born and raced in the eye of the storm. A generation that knows and wants to c hange the world\, that claims for more rights\, benefits and comfort. A ge neration becoming\, if not already become\, the economic machine to move o ur financial and banking system. A System with more than 200 years\, reluc tant to adapt this new Era\, will have what it takes to provide ethical\, innovative\, equally\, useful\, and human centered financial services? Fin techs have already started and succeeding.\n

\n\n

\nI’m a young\, committed woman\, always caring for others\, and thinking of the next ste ps on how to solve financial inclusion problems. I am the Vice President o f Market Entry at Kuelap\, Inc. In where my team calls me ‘a force of natu re’\, since I am convinced that as part of my generation everybody should be a change agent\, improving the state of the world\, and doing the littl e things today that will have an incredible impact tomorrow. I am a proble m-solution driven person. Always searching for a way to improve and find a solution to any given problem. I am highly committed to Social Inclusion\ , and work to fulfill that vision bringing Financial Inclusion to people a ll over the world. Before Kuelap\, I worked at the Mifos Initiative\; wher e I was the Account Manager. The Mifos Initiative is a non-profit open-sou rce and free software project\, with a presence in 37 countries\, with mor e than 500 Finance Institutions using the software and reaching over 6 mil lion final users. I have worked for and committed to projects that promote Social Inclusion as a volunteer for the past decade. As a former Law stud ent\, I was involved in projects that included the coordination of legal a ssistance of the largest free clinic of Uruguay\; project deployed by the Student’s Centre of Law School at the Universidad de la República. Since 2 013\, I am a Global Shaper\, an initiative of the World Economic Forum\, w hich gathers young leaders to create a high impact on social projects. In 2014\, I decided to bring my experience\, knowledge\, and enthusiasm on So cial Inclusion to my professional life and started to work on Financial In clusion\, as a key pillar to overcome poverty around the world. In that re gard\, I co-founded $ERO\, an Electronic Bank for the Base of the Pyramid in Uruguay\, which was the first Latin-America startup in obtaining a Cent ral Bank’s banking license.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-W1935@apachecon.com SEQUENCE:0 DTSTAMP:20200928T190012Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Running Fineract.dev like a Cloud Native SRE LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Vorburger\n\n

\n https://www.fineract.dev runs https://github.com/apache/fineract as a serv ice. This talk will give a peek behind the curtain of how that has been se t up\, detailing e.g. http://blog2.vorburger.ch/2020/05/fineractdev-cicd-f rom-github-to-google.html etc. It will mention the details of that cloud's used services\, but go easy on marketing the particular cloud (Google's) that Fineract.dev runs on. We will also lightly touch upon a few SRE princ iples from https://landing.google.com/sre/books\, and mention some Cloud N ative application architecture principles (hint: it's not all about micros ervices only).\n

\n\n

\nMichael is a long time open sourcerer who over the years has been involved in too many projects to enumerate. He st arted in FLOSS one fateful night many moons ago through a friendly interac tion with a maintainer on an IRC channel of what was then the Mifos projec t. Ever since\, he has been actively supporting the humanitarian open sour ce platform for financial inclusion that is now known as Apache Fineract i n a volunteer capacity. Currently employed by Google\, previously at Red H at\, this talk is given in a purely personal capacity.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-R1615@apachecon.com SEQUENCE:2 DTSTAMP:20200828T190237Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:BitRupee: Passwordless Authentication using ZKP Bitcoin Protocol LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSaransh Sharma\,\nAtharva Dhekne\,\ nAdvait Madhekar\n\n

\nIn this digital day and age\, passwords are no longer adequate. Users worldwide are victims of multiple malfeasanc es like brute force attacks\, injection attacks\, phishing\, unsafe creden tials\, data theft\, among others. The flaws of using passwords - which ha ve increasingly become predictable\, leave users vulnerable to data and id entity theft. Even the strongest passwords are easy to crack and prone to phishing nonetheless. Hence\, given all these nuisances\, there’s a need t o eliminate character-based authentication protocols\, which would ultimat ely benefit all developers as well as end-users. An implementation on Apac he Fineract will also be presented.\n

\n\n

\nSaransh Sharma
\nSaransh carries out research in multiple fields like mathematics and Ope n Source. He is a Researcher at Muellners\, as well as its non-profit arm Muellners Foundation. Saransh is a Core Committer of Apache Fineract and i s an active supporter of Fintech. \n
\nAtharva Dhekne
\nAtharva is a Research Fellow & Technical Writer for Muellners Foundation\, working on multiple projects in Fintech as well as other domains. Being an Open S ource enthusiast\, Atharva contributes to multiple OSS organizations - he is also a Technical Writer & Core Committer at WordPress.org. Atharva is c ompleting his undergraduate Computer Engineering studies at the University of Pune (SPPU).\n
\nAdvait Madhekar
\nAdvait is a Research Fell ow & Technical Writer for Muellners Foundation\, working on multiple proje cts in Fintech as well as other domains. He is completing his undergraduat e Mechanical Engineering studies at the University of Pune (SPPU).\n\n\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:AI for All: Democratizing Data Science for Financial Inclusion LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJeremy Engelbrecht\, Lalit Mohan\, Ed Cable\n\n

\nIn this session members of the community working group leading artificial intelligence and machine learning will outline t he vision and roadmap for leveraging Apache Fineract to democratize data s cience for financial inclusion. They’ll share case studies highlighting ho w members of the community are currently pioneering tools for explainable decision making\, enhance customer experience\, and micro-analytics. The r oadmap will lay out how to evolve the Apache Fineract platform to support credit scoring and origination tools\, targeted segmentation and predictiv e product insights\, KYC\, AML\, and accelerated onboarding\, fraud detect ion\, chatbots with natural and regional language support\, customer churn prediction\, and more.\n

\n\n

\nJeremy Engelbrecht:
\nJerem y has 20 years experience in the design of software systems from end to en d (SDLC). He has worked for two banks in Southern Africa\, First National Bank and Standard bank and also worked for a JSE listed insurance company\ , Clientele. He was the Solutions Architect for an award-winning European fintech company called Mybucks. He was selected to be the CTO to start the first digital bank in Saudi Arabia. He has co-founded LNDR which is a fin tech company that has started the first digital bank in Swaziland and is t he chosen fintech company of choice for a large bank in south africa. He i s currently completing his MSc in advanced computer science at the Univers ity of Liverpool\, majoring in Artificial Intelligence with a dissertation based on proving a hypothesis to use DBN(Deep Belief Networks) to do cred it scoring using large unstructured datasets.
\nLalit Mohan:
\nL alit is pursuing his PhD in Computer Science & Engineering at IIIT Hyderab ad in the area of Information Retrieval\, Software Engineering\, ML and NL P. Lalit has been a GSOC mentor at Mifos/Fineract for the last 2 years. La lit has 23+ Years of IT experience at Infosys\, Wells Fargo\, IDRBT (India ’s central bank - Reserve Bank of India - Technology research institute). He has published papers on Credit risk evaluation using ML Models\, Digita l Banking\, FAQs on Cloud Computing for Banks\, API Banking\, Open source for Banks and other articles. He is also a member of Banking Industry Arch itecture Network (BIAN). Some of the key projects executed include a) Trea sury Management for World Bank b) Establishment of Indian Banking Communit y Cloud c) Deployment of Payment Systems in a SaaS model to reduce the cap ital expenses for cooperative Banks.
\nEd Cable:
\nEd has been a part of the Mifos project since 2007 in its early days at Grameen Foundat ion. He oversaw the open source community\, connecting its members worldwi de with the tools\, support\, and engagement needed to build and use Mifos . Leading the growth of this burgeoning community\, he saw the dedication and persistence of its members and decided to found COSM (now the Mifos In itiative) to unite their efforts and help them collectively fulfill the vi sion Grameen Foundation set out to achieve. Prior to this\, he graduated f rom the Wharton School at the University of Pennsylvania where he led mark eting for the nation’s largest student-run credit union and discovered his passion for technology-driven international development in their budding social entrepreneurship program. When he’s not watching over the Mifos com munity\, he’s tending to another community of sorts\, his mini-farmhouse o f animals – chickens\, bunnies\, dogs\, goats\, cats\, birds\, and fish.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-R1735@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:To Infinity and Beyond: Scaling Fineract for the Enterprise. LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAvik Ganguly\, Nayan Ambali\, Ed Ca ble\, Istvan Molnar\, Victor Romero\n\n

\nThis panel discussion will showcase a number of institutions that have extended to Fineract to support different use cases at scale including lending\, payments\, wallet s\, and digital credit. They will discuss the platforms enhancements they made along with the DevOps strategies to help scale Fineract to reach mill ions. They will also explore the practical ways in which the community cou ld achieve greater performance in the upstream Fineract codebase\, the roa dmap to achieve the same\, and replicable tools the community can collabor ate on to continually improve performance on both generations of Fineract. \n

\n\n

\nAvik Ganguly:
\nAvik Ganguly is the founder of Fyn arfin\, a fintech company building scalable enterprise solutions on top of Apache Fineract. Avik is a Fintech specialist and open source evangelist who picked up his trade while working with Mifos\, Conflux and Novopay.
\nNayan Ambali:
\nNayan is the Co-Founder and CEO of Finflux\, a l eading Mifos partner\, whose cloud distribution built on top of the Finera ct APIs is reaching more than several dozen financial institutions serving more than 3M clients and a portfolio greater than $2.5B USD.
\nEd Ca ble:
\nEd has been a part of the Mifos project since 2007 in its earl y days at Grameen Foundation. He oversaw the open source community\, conne cting its members worldwide with the tools\, support\, and engagement need ed to build and use Mifos. Leading the growth of this burgeoning community \, he saw the dedication and persistence of its members and decided to fou nd COSM (now the Mifos Initiative) to unite their efforts and help them co llectively fulfill the vision Grameen Foundation set out to achieve. Prior to this\, he graduated from the Wharton School at the University of Penns ylvania where he led marketing for the nation’s largest student-run credit union and discovered his passion for technology-driven international deve lopment in their budding social entrepreneurship program. When he’s not wa tching over the Mifos community\, he’s tending to another community of sor ts\, his mini-farmhouse of animals – chickens\, bunnies\, dogs\, goats\, c ats\, birds\, and fish.
\nIstvan Molnar:
\nIstvan is Partner and Architect for DPC Consulting\, an enterprise Java consultancy from Budape st\, Hungary that has decades of experience implementing banking and real- time payment solutions at scale. Istvan has led the deployment of Mifos an d Fineract at a bank in Germany as well as banks in SE Asia. Building off of the experience implementing real-time payment systems for Singapore and Hungary\, Istvan has lead the design and architecture of Payment Hub EE\, a powerful bridge and microservices workflow orchestration tool spearhead ed by the Mifos Initiative\, for integrating Fineract with real-time payme nt systems like Mojaloop.
\nVictor Romero:
\nVictor is a profess ional with more than 20 years of experience in the Information Technology sector\, throughout which he has consolidated knowledge and forged technic al and administrative skills focused on providing solutions\, meeting nati onal and international quality standards\, focused mainly on the Financial sector. He has participated in Development\, Operations\, Management and Control areas\, the experience in these areas gives him a broad vision to take strategic decisions that enhance the social and business objectives p romoted by technology demanded by financial institutions\, always acting w ith a high sense of professional ethical values. He co-founder of Finteche ando where we have been working with the Mifos Initiative using Fineract 1 .x/CN for providing digital solutions for the Mexican financial institutio n which serve the population at an enterprise level.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-R1815@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:OpenG2P: Open Source Building Blocks for Digitizing Large Scale Ca sh Transfers LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSalton Massally\, Keyzom Massally\, Steve Conrad\, James Dailey\, Ed Cable\n\n

\nThe global COVID- 19 pandemic has unleashed an unprecedented period of economic turmoil and instability that governments will have to overcome. Those in the informal economy are impacted the most and governments must be able to effectively mobilize large scale cash transfer programs to help them survive. Emerging from the government of Sierra Leone\, based on their experiences digitizi ng payments to healthcare workers during the Ebola crisis\, OpenG2P is a f ramework\, community of practice\, and set of open source building blocks to address the common pervasive challenges making it difficult to scale go vernment to person payments. Fineract acts as the of the key building bloc ks managing wallets\, accounts\, and stores of value for individuals. This panel will explore the full open source stack for Open G2P and lessons le arned in implementing large scale cash transfer programs.\n

\n\n

\nSalton is a believer in the transformative impact of technology. He is e xperienced in building and scaling tech-based solutions to expanding acces s to much-needed information & services to the un(der)served\, across Afri ca. Focused on Digital Financial Services (DFS) & Financial Technologies a s a driver for Financial Inclusion\, his competencies include Financial In clusion Thought Leadership\, Product Development and Delivery and Software Engineering: At 23\, he founded iDT Labs\, https://idtlabs.xyz\, known fo r its expertise in developing & applying practical and sustainable technol ogy to key sectors such as finance\, agriculture\, justice\, & health thro ugh effective leadership\, team-building\, project planning\, and delivery \, fundraising\, and multilateral stakeholder management. During the heigh t of West Africa's 2014 Ebola crisis\, he led the technology component of the Ebola response\, implementing an innovative program that combined tech nology\, inclusive finance\, public health\, and responsive governance. We broke new grounds in the use of mobile wallets\, cloud computing\, and op en source technology to deliver scale\, efficiency and achieve transparenc y of payments in crisis\, playing a critical role in preventing the collap se of the Ebola response and recovery efforts\, and the health sector as a whole in Sierra Leone. In recognition\, iDT Labs was awarded the 2016 UPS International Disaster Relief Award\, and Salton\, the 2017 Queen's Young Leader Award.\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-R1855@apachecon.com SEQUENCE:1 DTSTAMP:20200909T142006Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:Real-Time Payments: Enabling a Connected\, Cashless\, and Inclusiv e Society LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nIstvan Molnar\, Godfrey Kutumela\n< /strong>\n

\nAcross the globe\, real-time payment systems are being roll ed out providing a higher degree of convenience\, transparency\, and effic iency enabling secure\, cashless\, and more inclusive economies transformi ng commerce at all levels - consumers\, business\, and governments. The Ap ache Fineract architecture will need to continue to evolve and keep pace w ith how value is exchanged. The Mifos Initiative has been aligning its dev elopment on Apache Fineract and Mifos to equip governments and institution s to effectively participate in the real-time systems being built in accor dance with emerging standards like the Gates Foundation’s Level One Princi ples and complementary open source systems like Mojaloop enabling real-tim e interoperable payments. This session will explore the emerging trends an d standards around real-time payment systems including a closer look at th e ecosystems in a couple of countries\, an overview of these guiding princ iples illustrated by Mojaloop as a reference implementation and a showcase and roadmap of the Payment Hub EE - the open source bridge and microservi ces orchestration layer Mifos is building to seamlessly enable accounts an d wallets managed on Fineract to initiate transactions over modern real-ti me payment rails via mobile channels and Open APIs.\n

\n\n

\nIstv an is Partner and Architect for DPC Consulting\, an enterprise Java consul tancy from Budapest\, Hungary that has decades of experience implementing banking and real-time payment solutions at scale. Istvan has led the deplo yment of Mifos and Fineract at a bank in Germany as well as banks in SE As ia. Building off of the experience implementing real-time payment systems for Singapore and Hungary\, Istvan has lead the design and architecture of Payment Hub EE\, a powerful bridge and microservices workflow orchestrati on tool spearheaded by the Mifos Initiative\, for integrating Fineract wit h real-time payment systems like Mojaloop\n
\n\nGodfrey has 20 years of Technology Consulting experience specializing in Fintech\, Cybersecurit y\, DevSecOps\\BizSecOps\, Cloud Native and General Solution Architect. Ex tensive background working with large scale\, high-profile systems integra tion and development projects that span a customer’s organization\, and ex perience designing robust solutions that bring together multiple platforms . He is passionate about the use of technology and its applications in eve ry aspect of humanity in order to advance the livelihood and economic cond itions of the under developed world. My interests lie in helping businesse s create and deliver value through innovative application of information a nd communication technologies with an outside-in approach\, focusing on co nsumer and market needs\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-fineract-R1935@apachecon.com SEQUENCE:0 DTSTAMP:20200924T125613Z DTSTART:20201001T193500Z DTEND:20201001T201500Z SUMMARY:Fineract CN improvement proposal LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKevin Madhu\, Saransh Sharma\n\n

\nWith most of the projects showing negligible or no activity at all for about an year\, the fineract-cn project looks so close to being lo oked upon as an abandoned beast. And because the difficulties one has to g o through to tame this beast\, not many people get to enjoy the real beaut y it really is. With our work\, we hope to make a difference to this curre nt state of the project and to attract more people into getting to know th e project and make contributions by making it easier for them so that we c an steer the project towards a more mature\, stable\, complete and releasa ble version.\n

\n\n

\nKevin Madhu:
\nDeveloper implementing Finscale version inspired from fineract CN
\nSaransh Sharma :
\n Technical writers working for Muellners Foundation\n

CATEGORIES:Fineract URL;VALUE=URI:https://apachecon.com/acah2020/tracks/fineract.html#R1935 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-T1615@apachecon.com SEQUENCE:3 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:A Caching Approach to Data Transformation of Legacy RDBMS LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGregory Green\n\n

\nThis session will a test driven development approach to building data domains where the source system of record is a legacy Relation Database Management System. The initial code will focus on mainframe based DB2 migration. The pattern will be applicable to similar solutions using Oracle\, Sybase and other similar traditional relational databases. The talk will highlight t he pros and cons of different styles of data pipelines. For example\, Day 0 initial loads\, Change Data Capture\, Event based streams and scheduled pulled based tasks. The following are the highlighted technologies\; - Apa che Geode - Spring Data Geode - Spring Data/JDBC - Kakfa - Spring Cloud St ream - Spring Task/Spring Batch - Spring Cloud DataFlow\n

\n\n

\n Senior Consultant with over 23 years of software development and architect ure experience. Specializing in application transformation from legacy/mon olith systems to microservices cloud-native applications with a focus on s calable\, highly available and self-healing cloud-native data platforms.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-T1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:How I contributed the transactionality in WAN replication feature to Apache Geode LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAlberto Gomez\n\n

\nIn t his talk I will describe the use case that drove me to develop the \"Trans actionality in WAN replication\" feature and then I will sketch the techni cal solution implemented. In a second part\, I will walk you through the p rocess I followed to contribute the feature\, from the point of view of a recent member of the Apache Geode Community.\n

\n\n

\nSoftware en gineer with more than 20 years of experience in the telco world. Husband\, father of three and stylish tennis contender.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-T1735@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Introduction to Apache Geode Through Spring Data LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPatrick Johnson\n\n

\nAp ache Geode is a distributed in-memory data-grid designed with speed\, conc urrency\, and scalability in mind. Apache Geode can be used as a system of record\, cache\, and much more. Spring Data is an extension of the Spring Framework that adds useful abstractions to make working with data simpler with less boilerplate code. In this presentation\, Patrick will dive into what Apache Geode is\, how it works\, what it does\, and how to get start ed using it with Spring Data for Apache Geode.\n

\n\n

\nA (semi)r ecent graduate of Oregon Institute of Technology\, Patrick is employed as a Software Engineer at VMware in Portland\, Oregon\, where he works primar ily on Spring Data for Apache Geode.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-T1815@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Got Javascript Apps? We Can Do That! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKaren Miller\, Blake Bender\n\n

\nYour app is written in Javascript\, and that's not going to chan ge. Your app needs a cache\, and you want to use Apache Geode for your cac hing layer. Until now\, you've been out of luck. VMware has developed a No de.js Client for Apache Geode. The donation to Apache Geode is in progress . Stay tuned! You can keep your Javascript app\, and use the Node.js Clien t to interact with an Apache Geode cluster.\n

\n\n

\nKaren Miller :
\nKaren is the current Apache Geode PMC chair. She writes technical documentation for VMware\, where she pursues opportunities to promote the understanding of Apache Geode. Prior to VMware\, Karen taught Computer Sc iences courses at the University of Wisconsin-Madison and is also a textbo ok author.
\nBlake Bender:
\nBlake is the Apache Geode Native pr oject anchor. He works for VMWare in this capacity\, and in the same role at Pivotal prior to the VMWare acquisition. He previously worked at Intel Architecture Labs in Hillsboro\, OR\, and at Microsoft in Redmond\, WA\, s pecializing in media and contributing to Microsoft Silverlight\, Media Fou ndation\, DirectX\, and several releases of Windows.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Running a Apache Geode on Kubernetes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Oleske\, Aaron Lindsey\n\n

\nAs application developers move workloads to Kubernetes\, they expect data services to run on Kubernetes alongside their applications. K ubernetes excels at running stateless workloads\, but how does it handle c omplex stateful applications such as Apache Geode\, a distributed in-memor y database? We will describe challenges faced while building a Geode opera tor for Kubernetes\, including controlling pod terminations\, working with a dynamic network\, and ensuring state management during the lifecycle of the deployment. We will explain the solutions we took to control these ch allenges\, and dive into how we tested these solutions. You will leave wit h an understanding of how we moved a system designed to run on bare metal to a Kubernetes environment\, uniting your workloads with your data servic es.\n

\n\n

\nMichael Oleske:
\nMichael is a software enginee r on Apache Geode and Tanzu GemFire. He works on making Geode both run wel l and easy to deploy for Kubernetes.
\nAaron Lindsey:
\nAaron wo rks as a software engineer on Apache Geode and VMware Tanzu GemFire\, focu sing on making Geode run well on Kubernetes. Outside of work\, he enjoys h iking and backpacking in the Pacific Northwest mountains\, and volunteerin g with his local community and church.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Apache Geode: Exposing just one ip for all your gateway receivers in K8s LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAlberto Bustamante\n\n

\ nApache Geode uses gateway senders and gateway receivers for events replic ation in multisite configuration. Each receiver usually has its own ip\, w hich has some drawbacks in cloud environments as Kubernetes. In this talk I would like to show why we decided to expose all our gateway receivers wi th the same ip\, the problems we found\, the solution proposed and how I c ontributed it to Geode.\n

\n\n

\nAlberto is a Computer Science en gineer by Carlos III University of Madrid\, with a Master on Software Craf tsmanship by Polytechnic University of Madrid. He has been working at Eric sson Spain since 2008 mainly as Software Developer. Since 2019 he works as Open Source Developer contributing to Apache Geode\, a data management pl atform ( in-memory data grid ) that provides real-time\, consistent access to data-intensive applications throughout widely distributed cloud archit ectures.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:vMotion and Apache Geode: Investigating the impact of live migrati on of virtual machines on an in-memory data grid LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nNabarun Nag\n\n

\nAvoidi ng downtime during maintenance or unforeseen machine issues is paramount f or mission-critical\, ready and available systems. To achieve this goal\, VMware vSphere vMotion provides the capability for a zero-downtime live mi gration of virtual machine workloads from one server to another. During th e entire duration of migration\, all applications continue running and pro viding access to users. This feature can be also be automated using Dynami c Resource Scheduler which places a virtual machine in an optimal location in the server cluster. Pivotal Cloud Cache is an in-memory key-value stor e powered by Apache Geode\, which is responsible for responding to large v olume of concurrent read/write requests without compromising throughput an d latency. Pivotal Cloud Cache also serves multiple use cases like event p rocessing\, transaction and session state caching\, etc. in industries lik e finance and travel. To evaluate the impact of vMotion migration of virtu al machines hosting Cloud Cache servers\, we devised experiments where we deploy a Cloud Cache cluster using the Pivotal Platform in VMware’s Soluti ons lab. We then continuously migrate the virtual machines using vSphere S DK\, while the cluster is under read and write workloads. We measure the i mpact on latency and throughput and also monitor that no members are being kicked out of the distributed system due to lack of response to heartbeat messages during the migration phase. This paper discusses the experiment design and results in detail.\n

\n\n

\nNabarun has been a code co ntributor and PMC member for Apache Geode since 2016\, after graduating fr om University of Wisconsin-Madison. Prior to that\, he worked for Samsung Research Institute. In his spare time\, he likes to explore Portland's foo d scene and playing Apex Legends and Overwatch\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-geode-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Improving the Performance of Apache Geode Persistence Recovery LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJianxia Chen\n\n

\nApach e Geode offers super fast write-ahead-logging (WAL) persistence with a sha red-nothing architecture that is optimized for fast parallel recovery of n odes or an entire cluster. In this talk\, we will first introduce how Geod e disk stores work. Then we will present the recent work to improve the pe rformance of persistence recovery. With the analysis of Geode logs\, we fi nd that the performance of persistence recovery can be significantly impro ved by unblocking some of the server initialization threads and paralleliz ing the process of disk stores recovery. Our experiments have proved that persistence recovery becomes remarkably more scalable and efficient with t he improved process.\n

\n\n

\nJianxia is a PMC member and committ er of Apache Geode. He enjoys working on open source software.\n

CATEGORIES:Geode URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geode.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Apache Geospatial: Open Source and Open Standards LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGeorge Percivall\n\n

\nT he Apache Geospatial Track provides the latest in applying Apache Projects to geospatial data and processing. Beginning in 2016\, the geospatial tra ck has provided a venue for geospatial applications using open source from Apache and other open source foundations. The Geospatial Track includes a focus on the use of open standards to enable interoperability and code re use between independent software developments. The Geospatial Track for Ap acheCon 2020 will include projects from Apache Software Foundation as well as from LocationTech Technology ( http://locationtech.org) and the Open S ource Geospatial Foundation (https://www.osgeo.org/). Open standards will be included from the Open Geospatial Consortium (http://www.ogc.org/). Ope n standards enables reuse of common elements for geospatial information an d processing resulting in increased productivity\, lower interoperability friction\, and higher data quality. Standards for Coordinate Reference Sys tems (CRSs)\, spatial geometries and data arrays used for projects with ge ospatial content will be described based on open source projects and open standards. Emphasis is placed on the use of open standards including the e merging baseline of OGC APIs. OGC APIs are being defined for geospatial re sources\, e.g.\, maps\, tiles\, features\, coverages. Developed using Open API\, the APIs can be implemented in a number of languages and patterns. T he presentation will be describe the state of implementations and plans fo r standardization. The modular structure enables flexibility for developer s to reuse OGC APIs in their APIs. If open source for geospatial is of int erest to you\, join the discussion on geospatial@apache.org.\n

\n\n

< em>\nGeorge Percivall serves as CTO and Chief Engineer of the Open Geospat ial Consortium (OGC). As CTO he works with OGC members on strategic techno logy across OGC Programs and leads OGC Technology Forecasting. As Chief En gineer\, the OGC Architecture Board. Prior to OGC\, Mr. Percivall was Chie f Engineer with Hughes Aircraft for NASA's Earth Observing System Data and Information System\; Principal engineer for NASA's Digital Earth Office\; he applied Systems Engineering on the General Motors’ EV1 program and a c ontrol system engineer on Hughes satellites. He holds a BS in Engineering Physics and an MSEE from the University of Illinois.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-W1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Visualize Apache SIS capabilities with raster data LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMartin Desruisseaux\n\n

\nApache SIS is a Java library for helping developers to create their own geospatial application. SIS follows closely international standards publis hed jointly by the Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO). But the core standards implemented by SIS are abstracts\, and their practical use are non-obvious for develo pers unfamiliar with OGC/ISO conceptual models. Recently a JavaFX applicat ion is being developed for showing Apache SIS in action. Its main purpose is still to provide building blocks that developers can use in their own a pplications\, but the SIS application can also be used as-is for navigatin g in some raster data. Using that application\, some ISO 19115 elements (t he common metadata structure used by SIS for representing information stor ed in headers of various data formats) get a visual aspect. Some ISO 19111 concepts (the model for reference systems and operations) can be more eas ily explored. Jacobian matrices (a SIS feature) can been seen in action in the context of map projections. Apache SIS is an implementation of GeoAPI 3.0.1 interfaces\, which are developed by OGC. Another GeoAPI implementat ion created during the year is a binding to PROJ 7 C++ API. We will show h ow GeoAPI allows the use of alternative implementation such as PROJ 7 in a Java application. GeoAPI interoperability with the Python language (work in progress) will also be demonstrated. Finally recent development of Apac he SIS support of raster data (work in progress) will be presented\, with an introduction to its API. The emphasis will be on Earth observation data .\n

\n\n

\nDid a Ph.D thesis in oceanography\, but have continuou sly developed tools for helping analysis work. Used C/C++ before to switch to Java in 1997. Develop geospatial libraries since that time\, initially as a personal project then as a GeoTools contributor until 2008. Now cont ributing to Apache SIS since 2013. Attend to Open Geospatial Consortium (O GC) meetings about twice per year in the hope to follow closely standard d evelopments and improve Apache SIS conformance to those standards. Work in a small IT services company (Geomatys) specialized in development of geop ortals. Geomatys is an OGC member and develop a stack of open source softw are for spatial applications\, with Apache SIS as the foundation to which Geomatys contributes actively.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-W1735@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:pygeoapi: an OSGeo community project implementing OGC API standard s LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTom Kralidis\, Francesco Bartoli\n< /strong>\n

\nThe proliferation of REST as an architectural style as well as OpenAPI has resulted in broader adoption of a leaner service contract and the OGC developing a new generation of API specifications in support o f discovery\, access\, visualization and processing of geospatial data. Th ese efforts are aimed to lower the barrier to implementation\, especially for mass-market/non-geospatial developers. pygeoapi is an OGC Reference Im plementation compliant with the OGC API - Features specification. Implemen ted in Python\, pygeoapi supports many other OGC APIs via the Flask web fr amework and a fully integrated OpenAPI structure. Lightweight\, easy to de ploy and cloud-ready\, pygeoapi's architecture facilitates publishing data sets and processes from multiple sources. Implementations of other OGC API s are in progress for the 1.0 roadmap\, including gridded/coverage data (O GC API - Coverages)\, search (OGC API - Records)\, and vector/map tiles (O GC API - Tiles). pygeoapi is a community project of the Open Source Geospa tial Foundation (OSGeo). pygeoapi follows a clear separation structure wit h a view\, provider/plugin and entry point module. The view approach allow s for easy integration with other Python web frameworks like Starlette and Django. The provider abstracts connectivity to numerous data sources (CSV \, SQLite3\, GeoJSON\, Elasticsearch\, GDAL/OGR) and provides extensibilit y to support additional formats\, databases\, object storage and more. Thi s presentation will provide an overview of pygeoapi\, current status and n ext steps as part of the evolution of the project.\n

\n\n

\nTom i s a Senior Systems Scientist for the Meteorological Service of Canada and is a longtime proponent of spatial data infrastructure\, interoperability\ , open standards and open source. He is chief architect of the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) and MSC's GeoMet platform o f real-time and archive weather\, climate and water APIs. Tom is active in OGC and is currently co-chair of the OGC API - Records SWG. He is also th e chair of the UN World Meteorological Organization Expert Team on Metadat a. Tom is has contributed to numerous FOSS4G projects such as QGIS and Map Server. He is the founder of the pycsw and pygeoapi projects. He is a Char ter Member of OSGeo and currently serves on their Board of Directors.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Enabling geospatial in big data lakes and databases with LocationT ech GeoMesa LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJames Hughes\n\n

\nMany of the Apache projects serving the big data space do not come with out of the box support for geospatial data types like points\, lines\, and polygo ns. LocationTech GeoMesa has provided add-on support to Apache database pr ojects such as Accumulo\, Cassandra\, HBase\, and Redis crafting spatial a nd spatio-temporal keys. In addition to distributed databases\, GeoMesa ha s enables spatial storage in many of the popular Apache file format projec ts such as Arrow\, Avro\, Orc\, and Parquet. This talk will review the bas ics of big geo data persistence either in a data lake or in a database\, a nd provide an overview of the benefits (and limitations) of each technolog y.\n

\n\n

\nJim Hughes applies training in mathematics and comput er science to build distributed\, scalable system capable of supporting da ta science and machine learning. He is a core committer for GeoMesa\, whic h leverages HBase\, Accumulo and other distributed database systems to pro vide distributed computation and query capabilities. He is also a committe r for the LocationTech projects JTS and SFCurve and serves a mentor for ot her LocationTech and Eclipse projects. He serves on the LocationTech Proje ct Management Committee and Steering Committee. Through work with Location Tech and OSGeo projects like GeoTools and GeoServer\, he works to build en d-to-end solutions for big spatio-temporal problems. Jim received his Ph.D . in Mathematics from the University of Virginia for work studying algebra ic topology. He enjoys playing outdoors and swing dancing.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Map Serving with Apache HTTPD Tile Server Ecosystem (AHTSE) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDr. Lucian Plesea\n\n

\n AHTSE is a collection of Open Source Apache httpd modules that can be used independently or combined to implement high performance and scalable tile services. Developed for geospatial applications\, AHTSE can be used for o ther domains that need fast pan and zoom access to large datasets. AHTSE s ource code is available on GitHub\, licensed under Apache License 2.0 term s. Geospatial web services compatible with the OGC WMTS\, Esri REST and ti led WMS can be implemented. The tight integration with httpd results in ex ceptional scalability and reliability. The AHTSE development represents an evolution of the NASA original OnEarth server code. Examples of public se rvices that use AHTSE are NASA's WorldView server (https://worldview.earth data.nasa.gov/)\, Esri's Astro server (https://astro.arcgis.com) and Esri' s EarthLive server (https://earthlive.maptiles.arcgis.com) This session wi ll describe the core AHTSE concepts\, demonstrate some of the existing ser ver instances and provide sample server configurations.\n

\n\n

\n Dr. Plesea worked at NASA's JPL\, where he was a pioneer in developing geo spatial imagery using supercomputers and later transitioned to building ge ospatial web services. He built and maintained multiple generations of the well known JPL Onearth/OnMars/OnMoon geospatial image servers\, and was i nvolved in the early development of the NASA WorldWind system. Once the On Earth server technology was adopted and transitioned to the NASA EOSDIS as the core server technology behind the WorldView client\, Dr. Plesea trans itioned to Esri\, where his responsibilities include developing cloud arch itecture for the basemap geospatial services and develop cloud raster tech nologies. Dr. Plesea is also an active GDAL contributor and maintainer\, a nd is the principal OnEarth and AHTSE developer.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-W1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Lizmap to create Web Map ApplicationsEdit proposal LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRené-Luc DHONT\n\n

\nLiz map is an open-source application to create web map application\, based on a QGIS plugin and a Web Client. The project started in 2011 and the 3rd v ersion has been published in 2016. In 2019\, the project has to be adapted to QGIS 3. We will present the state of the project\, the connected proje cts as mapbuilder module and extension scripts\, and what is coming in the future.\n

\n\n

\nFounding 3Liz Open Source GIS Software Editor * Lizmap developer * QGIS Server maintener\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-R1615@apachecon.com SEQUENCE:2 DTSTAMP:20200810T213949Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Apache Spark Accelerated Deep Learning Inference for Large Scale S atellite Image Analytics LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDalton Lunga\, PhD\n\n

\ nWith volumes of acquired remotely sensed imagery growing at an exponentia l rate\, there is an ever-increasing burden of research and development ch allenges associated with processing this data at scale. In particular\, th e application of object detection models across large geographic areas pre dominantly faces three obstacles: (1) a lack of workflows for gathering re presentative training data and mitigating data bias\, (2) the inability of current machine learning algorithms to generalize across diverse sensor a nd geographic conditions\, and (3) the deployment and reuse of hundreds of unique models at scale. By considering the above challenges in a joint ma nner\, we formulate and present an efficient\, geographically agnostic fra mework for remote sensing imagery analysis at a global scale. The framewor k addresses the problem of bias-free data selection by mapping observed sa tellite images to a novel metric space rooted in the manifold geometry of the data itself\, forming natural partitions of similar data. Using these partitions to seed training\, the framework enables simpler\, localized mo dels to be developed\; alleviating the challenge of generalization seen by more complex models for larger geographic extents. In an agile manner the framework further exploits the inherent parallelism for dataflow\, and ha rnesses Apache Spark to implement distributed inference and training strat egies which are seen to favorably scale. We discuss the challenges and mer its of using Spark with current deep learning frameworks\, providing insig ht into solutions developed for overall workflow harmonization. As a test case study\, with no training data gathered for any entire country\, we de ploy the framework to detect buildings and roads\, over areas that spans t housands of square kilometers and covered by 26TB of satellite image data. Drawing understanding from the results of this study\, we finally present future directions which this exciting research may take.\n

\n\n

\nDalton is currently a research scientist in machine learning driven geos patial image analytics at ORNL. In this role he deploys machine learning a nd computer vision techniques in high performance computing environments\, focusing on creating imagery-based data layers of interest to various soc ietal problems e.g. enable accurate population distribution estimates and damage mapping for disaster management needs. He currently conducts resear ch and development in machine learning techniques and advanced workflows f or handling large volumes of geospatial data. Prior to ORNL\, Dalton worke d as machine learning research scientist at the council for scientific and industrial research in South Africa on a variety of projects. He received his PhD in electrical and computer engineering from Purdue University\, W est Lafayette\, IN\, US.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-R1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T213949Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:AutoRetrain: automated deep learning model training on imagery usi ng Apache Airflow and Apache Nifi.- LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCarlos Caceres\n\n

\nThe ability to automate model training is a complex subject that has recently received much attention in the deep learning community. Multiple workflow management systems have also begun gaining traction\, and are necessary i n order to orchestrate the necessary steps to make auto-retraining feasibl e. This work tackles model automation by making use of two such technologi es: Apache Airflow and Apache Nifi. Since both fields of automatic model t raining and the overarching field of AutoML are broad and complex\, this w ork seeks to show the utility of AutoML approaches on object detection in overhead imagery by a simple approach: integrating cycles of model retrain ing as data becomes available over time. Not only does this approach match the reality of data acquisition\, it also seeks to leverage information a s it becomes available and in so doing\, reduces the time lag from acquiri ng new data to extracting useful intelligence. This work tackles a few pro blems practitioners often encounter when involved in long-term\, deep lear ning projects. Questions include: 1). when to start a new round of trainin g\, 2). how to minimize the time complexity of training a deep learning ne twork\, and 3). how to tackle the problem of selection bias\, which occurs when training sets contain uneven probability across classes. The third a nd most complex question originates from the uneven distribution that may be present in the data. This bias occurs for a variety of reasons\, low sa mpling opportunities chief among them. Selection bias and other forms of d ataset bias are only a part of the learning problem as learning through ba ck propagation also allows the model to ignore uncertainty in its predicti ons. Instead\, certain scenarios have been helped by other techniques\, su ch as curriculum learning\, active-bias learning\, and hard example mining that focus training on easy\, uncertain\, and hard examples respectively. Retraining as described consists of training cycles\, where each cycle co ntains the whole data science pipeline – from data gathering\, data prepar ation\, to training\, and scoring. In order to automate this process for a production system\, it is first necessary to establish a reliable method to orchestrate the execution of individual pieces of the pipeline. To this end\, this work experimented with Apache Nifi and Apache Airflow\, two po pular data flow management tools. By combining them with a tracking tool s uch as Mlflow\, both Apache Nifi and Apache Airflow become extremely usefu l in managing retraining flows in a way that allows for reliable reproduci bility.\n

\n\n

\nCarlos Caceres:
\nMAXAR
\nCloud Comput ing for Gov & Milsatcom Applications from satellite data.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-R1735@apachecon.com SEQUENCE:14 DTSTAMP:20200810T213949Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:GeoSpark: Manage Big Geospatial Data in Apache Spark LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJia Yu\, Mohamed Sarwat\n\ n

\nThe volume of spatial data increases at a staggering rate. This talk comprehensively studies how GeoSpark extends Apache Spark to uphold massi ve-scale spatial data. During this talk\, we first provide a background in troduction of the characteristics of spatial data and the history of distr ibuted spatial data management systems. A follow-up section presents the v ital components in GeoSpark\, such as spatial data partitioning\, index\, and query algorithms. The third section then introduces the latest updates in GeoSpark including geospatial visualization\, integration with Apache Zeppelin\, Python and R wrapper. The fourth part finally concludes this ta lk to help the audience better grasp the overall content and points out fu ture research directions.\n

\n\n

\nJia Yu:
\nJia Yu is an As sistant Professor at Washington State University School of EECS. He obtain ed his Ph.D. in Computer Science from Arizona State University in Summer 2 020. Jia’s research focuses on database systems and geospatial data manage ment. In particular\, he worked on distributed data management systems\, d atabase indexing\, and data visualization. He is the main contributor of s everal open-sourced research projects such as Apache Sedona (incubating)\, a cluster computing framework for processing big spatial data.
\nMoh amed Sarwat :
\nMohamed is an assistant professor of computer science at Arizona State University. Dr. Sarwat is a recipient of the 2019 Nation al Science Foundation CAREER award. His general research interest lies in developing robust and scalable data systems for spatial and spatiotemporal applications. The outcome of his research has been recognized by two best research paper awards in the IEEE International Conference on Mobile Data Management (MDM 2015) and the International Symposium on Spatial and Temp oral Databases (SSTD 2011)\, a best of conference citation in the IEEE Int ernational Conference on Data Engineering (ICDE 2012) as well as a best vi sion paper award (3rd place) in SSTD 2017. Besides impact through scientif ic publications\, Mohamed is also the co-architect of several software art ifacts\, which include GeoSpark (a scalable system for processing big geos patial data) that is being used by major tech companies. He is an associat e editor for the GeoInformatica journal and has served as an organizer / r eviewer / program committee member for major data management and spatial c omputing venues. In June 2019\, Dr. Sarwat has been named an Early Career Distinguished Lecturer by the IEEE Mobile Data Management community.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-R1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Rethinking Earth Observation using Deep Learning LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSayantan Das\n\n

\nEarth observation is the gathering of information about the physical\, chemical \, and biological systems of the planet via remote-sensing technologies. W ith the advent of better compute\, deep learning based methods have come u p that are optimizing over existing remote sensing algorithms. In this tal k\, we shall go over some examples of Computer Vision tasks on Satellite I mages including showcasing of one of my key projects done under the Indian Space Research Organisation. Slides to my abstract: http://bit.ly/session zero-geo Talk will be divided into three parts: 1. Coverage of what remote sensing is and how deep learning technology is being leveraged for better ment 2. Project showcase of semantic segmentation and object and land use classification using Tensorflow/Pytorch. 3. Open Source tools and a small example of map visualization .\n

\n\n

\nI am Sayantan Das\, a fin al year undergraduate student. I am mentoring for Google Code-In 2019 in T ensorflow. This summer I completed my research internship at Space Applica tions Centre\,ISRO Ahmedabad. Currently doing a research internship at CVP R Unit\,ISI Kolkata. I am pursuing my bachelors in Computer Science & Engi neering from West Bengal University of Technology. I love to read\,review and reproduce research papers.\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-R1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:Bring Satellite and Drone Imagery into your Data Science Workflows LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJason Brown\n\n

\nOverhe ad imagery from satellites and drones have entered the mainstream of how w e explore\, understand\, and tell stories about our world. They are undeni able and arresting descriptions of cultural events\, environmental disaste rs\, economic shifts\, and more. Data scientists recognize that their valu e goes far beyond anecdotal storytelling. It is unstructured data full of distinctive patterns in a high dimensional space. With machine learning\, we can extract structured data from the vast set of imagery available. Ras terFrames extends Apache Spark SQL with a strong Python API to enable proc essing of satellite\, drone\, and other spatial image data. This talk will discuss the fundamentals ideas to make sense of this imagery data. We wil l discuss how RasterFrames custom DataSource exploits convergent trends in how public and private providers publish images. Through deep Spark SQL i ntegration\, RasterFrames lets users consider imagery and other location-a ware data sets in their existing data pipelines. RasterFrames builds on Ap ache licensed tech stack\, fully supports Spark ML and interoperates smoot hly with scikit-learn\, TensorFlow\, Keras\, and PyTorch. To crystallize t hese ideas\, we will discuss a practical data science case study using ove rhead imagery in PySpark.\n

\n\n

\nJason is a Senior Data Scienti st at Astraea\, Inc. applying machine learning to Earth-observing data to provide actionable insights to clients' and partners' challenges. He bring s a background in mathematical modeling and statistics together with an ap preciation for data visualization\, geography\, and software development.\ n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-geospatial-R1935@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T193500Z DTEND:20201001T201500Z SUMMARY:Massively Scalable Real-time Geospatial Anomaly Detection with Apa che Kafka and Cassandra LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPaul Brebner\n\n

\nThis presentation will explore how we added location data to a scalable real-ti me anomaly detection application\, built around Apache Kafka\, and Cassand ra. Kafka and Cassandra are designed for time-series data\, however\, it’s not so obvious how they can efficiently process spatiotemporal data (spac e and time). In order to find location-specific anomalies\, we need ways t o represent locations\, to index locations\, and to query locations. We ex plore alternative geospatial representations including: Latitude/Longitude points\, Bounding Boxes\, Geohashes\, and go vertical with 3D representat ions\, including 3D Geohashes. For each representation we also explore pos sible Cassandra implementations including: Clustering columns\, Secondary indexes\, Denormalized tables\, and the Cassandra Lucene Index Plugin. To conclude we measure and compare the query throughput of some of the soluti ons\, and summarise the results in terms of accuracy vs. performance to an swer the question “Which geospatial data representation and Cassandra impl ementation is best?”\n

\n\n

\nSince learning to program on a VAX 11/780\, Paul has extensive R&D and consulting experience in distributed s ystems\, technology innovation\, software architecture and engineering\, s oftware performance and scalability\, grid and cloud computing\, and data analytics and machine learning. Paul is the Technology Evangelist at Insta clustr. He’s been learning new scalable technologies\, solving realistic p roblems and building applications\, and blogging about Apache Cassandra\, Spark\, Zeppelin\, Kafka\, and Elasticsearch. Paul has worked at UNSW\, se veral tech start-ups\, CSIRO\, UCL (London\, UK)\, & NICTA. Paul has helpe d solve significant software architecture and performance problems for cli ents including Defence and NBN Co. Paul has an MSc in Machine Learning and a BSc (Computer Science and Philosophy).\n

CATEGORIES:Geospatial URL;VALUE=URI:https://apachecon.com/acah2020/tracks/geospatial.html#R1935 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Groovy update: What's new in Groovy 3.0 and coming in 4.0 LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPaul King\n\n

\nThis tal k looks at the latest features in Groovy from 3.0 and beyond. This include s the Parrot parser and a myriad of other new miscellaneous features. This will be the first ApacheCon talk going into details of the features plann ed for Groovy 4.0 including numerous large scale reworking effects and Gro ovy's response to features coming in JDK versions up to JDK 14. The talk o utlines a broad roadmap of how the new features are planned to be rolled o ut and the system requirements for each version.\n

\n\n

\nPaul Ki ng is a JavaOne Rockstar who has been contributing to open source projects for nearly 30 years. He is an active committer on numerous projects inclu ding Groovy\, GPars and Gradle. Paul speaks at international conferences\, publishes in software magazines and journals\, and is a co-author of Mann ing’s best-seller: Groovy in Action\, 2nd Edition. He is also VP Apache Gr oovy and Chair of the Apache Groovy PMC.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:What's in Groovy for Functional Programming LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nNaresha K\n\n

\nThe dire ctions in which popular programming languages are heading to is clear evid ence of the need for multiple programming paradigms. One such programming paradigm that is gaining attention these days is functional programming. G roovy too has embraced functional programming and provides a wide variety of features for a developer to code in the functional style. In this live coding session\, I demonstrate the functional programming features of Groo vy. We start with the higher-order function support in Groovy and see the benefits they offer. From the example\, we can observe that functional pro gramming is indeed idiomatic in several parts of Groovy. We then step into implementing functional composition\, currying\, memoizing tail-call opti mization\, and recursion. We conclude the session by understanding how dep endency injection works in functional programming. By the end of the sessi on\, developers understand how functional programming leads to concise and better maintainable code. Developers using Java learn additional support for functional programming in Groovy.\nAbout the speaker(s):\n

\n\n

< em>\nNaresha works as Developer\, Technical Excellence Coach and Cloud Tra nsformation Catalyst. He works with the developers to improve their profes sional practices to get better at developing maintainable applications tha t continuously deliver business value. He also helps teams to architect so lutions for the cloud and migrate applications to cloud platforms. He has been developing enterprise software for more than 13 years. TDD\, Refactor ing\, Programming languages\, Cloud architecture and Continuous Delivery a re his current areas of interest. He is passionate about learning new tech nologies/ programming paradigms and applying them to solve business proble ms. Naresha is the founder organiser of Bangalore Groovy User Group. Nares ha has been a speaker at several conferences including GR8 Conf EU\, Funct ional Conf\, GR8 Conf India\, GIDS\, Java2Days Bulgaria\, Eclipse Summit\, Selenium Conf\, AWS Community Day\, and FOSSCON India.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Apache Groovy's Metaprogramming Options and You LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndres Almiray\n\n

\nApa che Groovy provides several ways to modify and update programs and classes by means of metaprogramming. Some of this options are available at runtim e\, some others at compile time\, and some are even reachable to other JVM languages. These options allow library and framework authors to design be tter integrations\, prototype new language constructs without grammar chan ges\, deliver powerful and and gratifying DSLs\, and more. Come to this ta lk to discover these options and learn how you can put them to work on you r projects.\n

\n\n

\nAndres is a Java/Groovy developer and a Java Champion with more than 20 years of experience in software design and dev elopment. He has been involved in web and desktop application development since the early days of Java. Andres is a true believer in open source and has participated in popular projects like Groovy\, Griffon\, and DbUnit\, as well as starting his own projects (Json-lib\, EZMorph\, GraphicsBuilde r\, JideBuilder). Founding member of the Griffon framework and Hackergarte n community event.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Effective Java with Groovy - How Language Influences Adoption of G ood Practices LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nNaresha K\n\n

\n'Effecti ve Java' presents the most effective ways of using language. However\, the adoption of these practices among Java developers is less than satisfacto ry. In my observation\, the effort required to implement them can be a bar rier to the adoption of these practices. Since Groovy runs on JVM\, most o f the suggestions from Effective Java are equally relevant for Groovy deve lopers. Groovy provides out of the box constructs for many of the recommen ded practices which can boost developer productivity. In this talk\, I wal k you through code examples that follow these good practices\, highlightin g the Groovy way of implementing the 'Effective Java' suggestions. As a pa rticipant\, you walk away\, appreciating the simplicity with which Groovy empowers the developers. The talk also provides food for thought - how a l anguage can influence its users to adopt good practices. Java users learn the techniques a language can use to reduce the friction to adoption of go od practices\, instead of coming up with a prescription on how to implemen t good practices. Developers familiar with Groovy understand the reason be hind the design of their favourite language features.\n

\n\n

\nNa resha works as Developer\, Technical Excellence Coach and Cloud Transforma tion Catalyst. He works with the developers to improve their professional practices to get better at developing maintainable applications that conti nuously deliver business value. He also helps teams to architect solutions for the cloud and migrate applications to cloud platforms. He has been de veloping enterprise software for more than 13 years. TDD\, Refactoring\, P rogramming languages\, Cloud architecture and Continuous Delivery are his current areas of interest. He is passionate about learning new technologie s/ programming paradigms and applying them to solve business problems. Nar esha is the founder organiser of Bangalore Groovy User Group. Naresha has been a speaker at several conferences including GR8 Conf EU\, Functional C onf\, GR8 Conf India\, GIDS\, Java2Days Bulgaria\, Eclipse Summit\, Seleni um Conf\, AWS Community Day\, and FOSSCON India.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:What would a new Groovy web console look like? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGuillaume Laforge\n\n

\n The venerable Groovy web console helps developers share snippets of groovy code. However\, it doesn’t really look fresh. Furthermore\, there are cer tain limitations that could potentially be lifted. What could a redesigned web console look like? Sometimes\, you’d like to pin a specific version o f Groovy: you might want to try the new Groovy 3 or are stuck with a 2.5.x version. Perhaps you are using @Grab to take advantage of some dependenci es in your script\, and you would like to test and run this snippet online . Is it possible? Let’s see what we can do for the long awaited v2 of the Groovy web console!\n

\n\n

\nAt Google\, Guillaume Laforge is Dev eloper Advocate for the Google Cloud Platform\, where he spreads the word about the rich set of products and services offered for developers wishing to take advantage of the cloud for their projects and businesses. Before joining Google\, at Restlet\, Guillaume was taking care of the Product Lea dership around the APISpark API management platform\, the Restlet Studio f or crafting Web APIs and the Restlet Framework for authoring restful appli cations. He is also leading the Developer Advocacy team\, to interact with developers using those projects. He's also well known for his deep involv ement with the Apache Groovy programming language and community over many years.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Favouring Composition - The Groovy Way LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nNaresha K\n\n

\nMost dev elopers I met agree that composition is better than inheritance. However\, in most codebases\, we see the use of inheritance where composition would have been a better design choice. Then why are the Java developers fallin g into this trap? It is easy to implement inheritance over composition\, I sn’t it? But we end up paying for the consequences in terms of reduced mai ntainability. Can language offer anything for the developers to implement compositions? In this presentation\, I walk you through what Groovy has to offer to make sure implementing composition is as easy as inheritance\, i f not simpler. I dive into three techniques for applying compositions in y our Groovy applications. We start with the technique of delegation and see how easy it is to implement compositions. We uncover the limitations of t his technique and introduce traits. After walking through plenty of code e xamples covering various aspects of using traits\, we briefly touch upon f unctional composition\, since Groovy also supports functional programming. \n

\n\n

\nNaresha works as Developer\, Technical Excellence Coach and Cloud Transformation Catalyst. He works with the developers to improv e their professional practices to get better at developing maintainable ap plications that continuously deliver business value. He also helps teams t o architect solutions for the cloud and migrate applications to cloud plat forms. He has been developing enterprise software for more than 13 years. TDD\, Refactoring\, Programming languages\, Cloud architecture and Continu ous Delivery are his current areas of interest. He is passionate about lea rning new technologies/ programming paradigms and applying them to solve b usiness problems. Naresha is the founder organiser of Bangalore Groovy Use r Group. Naresha has been a speaker at several conferences including GR8 C onf EU\, Functional Conf\, GR8 Conf India\, GIDS\, Java2Days Bulgaria\, Ec lipse Summit\, Selenium Conf\, AWS Community Day\, and FOSSCON India.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W0900@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:Groovy Hackathon LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGroovy Community\n\n

\nG roovy Hackathon\n

\n\n

\n...\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W0900 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Grails 4: Leveling Up Your Game LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nZachary Klein\n\n

\nGrai ls 4 takes the powerful and flexibility of the Grails framework to a new l evel\, with the latest versions of core frameworks like Spring 5.1\, Sprin g Boot 2.1\, Gradle 5\, and Groovy 2.5. Additionally\, Micronaut is now pa rt of the Grails foundation\, allowing many powerful features from Microna ut to be used natively within your Grails apps. In this talk\, we’ll look at how you can upgrade your Grails 3 project (with a little aside for Grai ls 2 projects as well) to Grails 4\, and get a taste of the new features a t your disposal in this exciting new release.\n

\n\n

\nZachary Kl ein has been practicing web development since 2010 and front-end developme nt since 2015. He's a contributor to both the Grails and Micronaut framewo rks\, as well as an instructor in Object Computing's training practice.\n< /em>

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:A Groovy Apache Fortress LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShawn McKinney\n\n

\nOf late\, some experimentation around a couple of API sets\, specifically Acc essMgr and AdminMgr in Apache Fortress\, a Role-Based Access Control syste m. The goal: To simplify and enhance the API interactions with Apache Fort ress specifically\, and security authorization systems in general. Admitte dly\, the fortress managers have grown over the years\, both in number of methods\, and their complexity. That is\, as new use cases or features pop up\, say dynamic constraints placed on roles\, new APIs must be invented to handle the new patterns. While this is a good thing\, the system’s evol ving/adaptable\, it’s bad from the standpoint of the number of methods use rs must learn\, and having to maintain new method entry points into the sy stem. Let’s take the [AdminMgr](https://github.com/apache/directory-fortre ss-core/blob/master/src/main/java/org/apache/directory/fortress/core/Admin Mgr.java) for example. It has over 50 public methods! Perhaps understandab le when one considers all of the entities that are being processed. But al so highly complicated and confusing for someone who is new to the space. W hat does groovy got to do with it? Enter Apache Groovy. As most of you kno w\, a dynamic scripting language that sits on top of the Java virtual mach ine. It accelerates development through the elimination of boilerplate and bringing some convenience to longstanding irritations in the platform. Th ings like checked exceptions\, brackets\, even semi-colons are no longer r equired. Here it brought the means to rapidly iterate over some design pat terns\, in order to find easier to use call/response flows. The new Groovy Admin manager has really just one method! Basically\, the ‘doIt' passing a n operation and entity names\, along with a map\, that contains the model (data). The map maps directly to the fortress model\, using the same entit y names and attributes. Similarly\, the Access manager can be simplified. No longer do we need many methods to create the session (for example). Aga in\, only one method\, start\, is needed. Again passing only a map contain ing the operands pertinent to the requires. One envisions how this API pat tern works in a service-based setting. Instead of calling a groovy functio n\, the client would invoke a service\, passing a JSON map with the data. But it's expensive to code API gateways and so Groovy helps in the prototy ping phase. It allows us to quickly get up to speed\, giving more freedom to experiment with new ideas\, possibly leading to more improvements and g rowth within the target systems themselves.\n

\n\n

\nCode Monkey\ n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Micronaut + Groovy LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSergio del Amo\n\n

\nIn this talk\, Micronaut committer\, Sergio del Amo introduces the framework and demonstrates how you can take your web application development to the next level with Micronaut features and Groovy succinctness to create power ful applications in the most productive way. It showcases how to use Micro naut with Groovy related technologies such as GORM\, Spock\, Geb. After th is talk you should have an understanding of what Micronaut development wit h Groovy looks like. No initial knowledge of Micronaut is required.\n

\ n\n

\nSergio del Amo feels genuinely empowered by Grails and how suc cinct and powerful Groovy is. After 6 years contributing his expertise to Grails applications\, Guides\, plugins\, and other related technologies\, Sergio assisted the 2GM team in the development of Micronaut. Since April 2015\, Sergio has been the author of Groovy Calamari\, a weekly newsletter about the Groovy Ecosystem: Grails\, Geb\, Gradle\, and Ratpack.\n CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Getting Groovy with Micronaut & JHipster LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nZachary Klein\n\n

\nJHip ster is a rapid development platform that makes it easy to build modern Ja vaScript frontends backed by JVM microservices\, including support for Mic ronaut. This allows you to produce microservice or monolith projects quick ly\, with plenty of customization options and a project structure that ill ustrates best practices when developing with Micronaut. As Micronaut is a JVM framework\, it is compatible with Groovy\, making it easy to use the G roovy language for tests (with Spock) and for general purpose application code\, even within standard Java project. In this talk we'll see how you c an add Groovy to your Micronaut project (using JHipster as a starting poin t\, but applicable even in your own non-JHipster projects)\, and still tak e advantage of Micronaut's powerful Dependency Injection and configuration support.\n

\n\n

\nZachary Klein has been practicing web developm ent since 2010 and front-end development since 2015. He's a contributor to both the Grails and Micronaut frameworks\, as well as an instructor in Ob ject Computing's training practice.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Taming your browser with Geb LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSergio del Amo\n\n

\nGeb is a Groovy layer on top of Selenium Webdriver. Geb integrates the Page O bject patter\, with CSS selectors and several DSLs to empower you to write browser test in an elegant and succinct way. In this beginner talk\, Serg io de Amo\, will introduce you to Geb and show you how to test a real Web. If you ever used Selenium\, Geb opens a world of possibilities.\n

\n\n

\nSergio del Amo feels genuinely empowered by Grails and how succin ct and powerful Groovy is. After 6 years contributing his expertise to Gra ils applications\, Guides\, plugins\, and other related technologies\, Ser gio assisted the 2GM team in the development of Micronaut. Since April 201 5\, Sergio has been the author of Groovy Calamari\, a weekly newsletter ab out the Groovy Ecosystem: Grails\, Geb\, Gradle\, and Ratpack.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-W1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Interacting with Ethereum Blockchains using Groovy and web3j LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKevin Wittek\n\n

\nEther eum is currently one of the most exciting technologies in the Blockchain d omain\, providing us with a Turing-complete distributed “world-computer” a nd a “rich statefulness”. But how do you actually interact with such a sys tem from within your applications and your code? Is it like using a databa se\, a web service\, the cloud? The answer is probably yes and no… In this talk\, we want to have a look under the hood and see some real code examp les of how to interact with Ethereum. We will use web3j\, a Java implement ation of the quasi-standard Javascript Etheuerem client library web3 and w e will use it not only with Java but with Groovy to get the flexibility an d ease of a scripting language onto the JVM. And we will get sciency and u se Groovy to get some data science work done with data we extract from Eth ereum\, demonstrating that Python and R aren’t the answer for everything.\ n

\n\n

\nTestcontainers co-maintainer and Testcontainers-Spock au thor\, passionate about FLOSS and Linux. Received the Oracle Groundbreaker Ambassador award for his contributions to the Open Source community. Soft ware Craftsman and testing fan. Fell in love with TDD because of Spock. Be lieves in Extreme Programming as one of the best Agile methodologies. Like s to write MATLAB programs to support his wife in performing behavioural s cience experiments with pigeons. Plays the electric guitar and is a musici an in his second life. After many years working in the industry as an engi neer\, Kevin is now doing his PhD at RWTH Aachen on the topic of verificat ion of Smart Contracts and is leading the Blockchain Research Lab at the I nstitute for Internet Security in Gelsenkirchen at the Westphalian Univers ity of Applied Sciences.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-groovy-R0900@apachecon.com SEQUENCE:2 DTSTAMP:20200810T213949Z DTSTART:20201001T090000Z DTEND:20201001T094000Z SUMMARY:Groovy and Data Science Workshop LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPaul King\n\n

\nGroovy i s a powerful multi-paradigm programming language for the JVM that offers a wealth of\nfeatures that make it ideal for many data science and big data scenarios.\n\n

    \n
  • Groovy has a dynamic nature like Python\, which means that it is very powerful\, easy to learn\, and productive. The langu age gets out of the way and lets data scientists write their algorithms na turally.
    • \n
  • Groovy has a static nature like Java and Kotlin\, whic h makes it fast when needed. Its close alignment with Java means that you can often just cut-and-paste the Java examples from various big data solut ions and they'll work just fine in Groovy.
    • \n
  • Groovy has first-cla ss functional support\, meaning that it offers features and allows solutio ns similar to Scala. Functional and stream processing with immutable data structures can offer many advantages when working in parallel processing o r clustered environments.
    • \n
\n\nThis workshop reviews the benefit s of using Groovy to develop data science solutions\,\nincluding integrati on with various JDK libraries commonly used in data science solutions\ninc luding libraries for data manipulation\, machine learning\, plotting and v arious big\ndata solutions for scaling up these algorithms.\n\nMath/Data S cience libraries covered include:\nWeka\, Smile\, Tablesaw\, Apache Common s Math\, Jupyter/Beakerx notebooks\, Deep Learning4J.\n\nLibraries for sca ling/concurrency include:\nApache Spark\, Apache Ignite\, Apache MXNet\, G Pars\, Apache Beam.\n

\n\n

\nPaul King is a JavaOne Rockstar who has been contributing to open source projects for nearly 30 years.\nHe is an active committer on numerous projects including Groovy\, GPars and Grad le. Paul speaks at\ninternational conferences\, publishes in software maga zines and journals\, and is a co-author of\nManning’s best-seller: Groovy in Action\, 2nd Edition. He is also VP Apache Groovy and Chair of the Apac he Groovy PMC.\n

CATEGORIES:Groovy URL;VALUE=URI:https://apachecon.com/acah2020/tracks/groovy.html#R0900 END:VEVENT BEGIN:VEVENT UID:acah2020-httpd-W1615@apachecon.com SEQUENCE:0 DTSTAMP:20200921T142837Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Apache's 25th Anniversary: a timeline of The Apache HTTP Server LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJim Jagielski\, Nick Vidal\n\n

\nThis year\, the Apache HTTP Server Project celebrates its 25th An niversary. In February 1995\, a small group of webmasters known as the Apa che Group came together with the goal of releasing a common distribution b ased on multiple \"patches\" to the NCSA HTTPd Server. The first public re lease of Apache was in April 1995 and\, after a major re-architecture\, Ap ache 1.0 was officially released in December 1995. Apache rapidly grew to become the most popular server on the Internet\, playing a key role in the growth of the World Wide Web and Open Source. The goal of this talk to pr esent a timeline of the Apache HTTP Server Project\, highlighting the most important milestones of this amazing software and community.\n

\n\n

\nJim Jagielski:
\nJim is a well known and acknowledged expert an d visionary in Open Source and IT\, an accomplished coder (in numerous lan guages) and frequent presenter/interviewee/consultant on all things Web an d Cloud related. He is best known as one of the developers and co-founders of the Apache Software Foundation and has served as President and Chairma n. He also served on the board\, as well as President\, for the Outercurve Foundation and was a director for the Open Source Initiative (OSI). Jim w orks for Uber as their Head of Open Source\, after stints at ConsenSys\, C apital One\, Red Hat\, VMware\, and others.
\nNick Vidal:
\nNick Vidal has been an open source advocate for over 15 years. He helped the O pen Source Initiative to celebrate the \"20th Anniversary of Open Source\" by organizing 100 activities across 40 major open source events worldwide .\n

CATEGORIES:httpd and the Web URL;VALUE=URI:https://apachecon.com/acah2020/tracks/httpd.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-httpd-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200914T143859Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Apache httpd and TLS/SSL certificates validation LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJean-Frederic Clere\n\n

\nWe will look to 2 different things here\, validation of the server certi ficate and validation of the client certificates. For the server certifica te we will show Let's encrypt and mod_md and speak about the new ACMEv2 pr otocol and OCSP stapling. For the client certificates we look to OCSP and other validations. Demo and quick start example will provided during the t al\n

\n\n

\nJean-Frederic has spent more than 20 years writing cl ient/server software. His knowledges range from Cobol to Java\, BS2000 to Linux and /390 to i386 but with preference to the later \;). He is committ er in Httpd and Tomcat and he likes complex projects where different langu ages and machines are involved. Borne in France\, Jean-Frederic lived in B arcelona (Spain) for 14 years. Since May 2006 he lives in Neuchatel (Switz erland) where he works for RedHat in the JBoss division on Tomcat\, httpd and cloud/cluster related topics.\n

CATEGORIES:httpd and the Web URL;VALUE=URI:https://apachecon.com/acah2020/tracks/httpd.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-httpd-R1735@apachecon.com SEQUENCE:0 DTSTAMP:20200914T143859Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:GraphQL in Apache Sling - but isn't it the opposite of REST? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBertrand Delacretaz\n\n

\nGraphQL is often presented as the opposite of REST\, but how could a que ry language be the opposite of an architectural style? Opposing technologi es and tools is rarely productive\, and although Sling is firmly based on REST principles\, it makes absolute sense to take advantage of GraphQL's r ich query language and \"one request does it all\" interaction model in Sl ing. In this talk we'll present a GraphQL scripting engine for Sling\, whi ch enables GraphQL queries either \"hidden\" on the server side\, for more control\, or provided by the clients in the more traditional way to provi de the full flexibility of the query language. Generating GraphQL schemas dynamically\, based on Sling Resource Types\, Sling Models and scripted sc hemas\, provides a lot of flexibility in mapping Sling content to the outs ide world and makes the query subsystem modular and flexible. This talk wi ll will help you make the best use of this new and exciting query language \, without compromising on the principles of adaptable and discoverable We b applications.\n

\n\n

\nBertrand Delacretaz works as a Principal Scientist for Adobe in Basel\, Switzerland. He's involved in software des ign and development for Adobe Experience Cloud products\, which use many o pen source modules\, mostly from Apache projects to which his teams contri bute extensively. Bertrand is a currently (2020-2021) on his eleventh term on the Apache Software Foundation's Board of Directors and has been activ e in the Foundation for about 20 years.\n

CATEGORIES:httpd and the Web URL;VALUE=URI:https://apachecon.com/acah2020/tracks/httpd.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-httpd-R1815@apachecon.com SEQUENCE:0 DTSTAMP:20200914T143859Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Apache Web Server Security Hardening LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrew Carr\n\n

\nIn my 2017 presentation I discussed hardening Apache Web server with Apache Tomc at behind it. There was a lot of interest in hardening Apache and recommen dations. We will review possible exploits and how proper mitigation can pr event breaches. Apache has security holes\, especially in older versions. While upgrading fixes a lot of problems\, there will always be exploits. W e want to demonstrate a system that is reliable and robust\, with the leas t amount of information exposed to the public. Additionally\, there will b e a review of some standard configurations you can build from to protect y our environment\n

\n\n

\nAbout: Andrew has been working in the I. T. industry since 1996 developing hardware\, network and software solution s to suit business needs and requirements. Leveraging open source software \, he has implemented enterprise software solutions for a number of large corporations while delivering training to staff\, both entry-level and exp ert. Currently\, Andrew works as a Consulting Enterprise Architect at Perf orce.\n\n

CATEGORIES:httpd and the Web URL;VALUE=URI:https://apachecon.com/acah2020/tracks/httpd.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-httpd-R1855@apachecon.com SEQUENCE:0 DTSTAMP:20200914T143859Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:Hardware-protected Keys for TLS: the httpd Angle LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSander Temme\n\n

\nUsing hardware-protected cryptographic modules (Hardware Security Modules or HS Ms) is a requirement in many applications for governments\, banking and fi nancial environments\, and others. This session will discuss these require ments\, provide an update on how the Apache HTTP Server's mod_ssl integrat e with HSMs\, and demonstrate how to configure httpd to use hardware-based keys for TLS.\n

\n\n

\nA long time contributor to the Apache HTT P Server\, in his copious spare time Sander Temme is the product manager a t nCipher Security\, an Entrust Datacard company\, for the nShield as a Se rvice Cloud-accesslble Hardware Security Modules.\n

CATEGORIES:httpd and the Web URL;VALUE=URI:https://apachecon.com/acah2020/tracks/httpd.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-ignite-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:In-Memory Computing Essentials For Software Engineers LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDenis Magda\n\n

\nAttend ees will be introduced to the fundamental capabilities of in-memory comput ing platforms that are proven to boost application performance and solve s calability problems by storing and processing unlimited data sets distribu ted across a cluster of interconnected machines. The session is tailored f or software engineers and architects seeking practical experience with in- memory computing technologies. You'll be given an overview (including code samples in Java) of in-memory concepts such as caches\, databases\, and d ata grids combined with a technical deep-dive based on Apache Ignite in-me mory computing platform. In particular\, we'll cover the following essenti als of distributed in-memory systems: * Data partitioning: utilizing all m emory and CPU resources of the cluster * Affinity co-location: avoiding da ta shuffling over the network and using highly-performant distributed SQL queries * Co-located processing: eliminating network impact on the perform ance of our applications\n

\n\n

\nDenis Magda is an open-source e nthusiast who started his journey in Sun Microsystems as a developer advoc ate and presently settled down at Apache Software Foundation in the roles of Apache Ignite committer and PMC member. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies to build successful open-source projects. You can be sure to com e across Denis at conferences\, workshops and other events sharing his kno wledge about the open-source\, community building\, distributed systems.\n

CATEGORIES:Ignite URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ignite.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-ignite-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Data Streaming using Apache Flink and Apache Ignite LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSaikat Maitra\n\n

\nApac he Ignite is a powerful in-memory computing platform. The Apache IgniteSin k streaming connector enables users to inject Flink data into the Ignite c ache. Join Saikat Maitra to learn how to build a simple data streaming app lication using Apache Flink and Apache Ignite. This stream processing topo logy will allow data streaming in a distributed\, scalable\, and fault-tol erant manner\, which can process data sets consisting of virtually unlimit ed streams of events. Apache IgniteSink offers a streaming connector to in ject Flink data into the Ignite cache. The sink emits its input data to th e Ignite cache. The key feature to note is the performance and scale both Apache Flink and Apache Ignite offer. Apache Flink can process unbounded a nd bounded data sets and has been designed to run stateful streaming appli cations at scale. Application computation is distributed and concurrently executed in clusters. Apache Flink is also optimized for local state acces s for tasks and does checkpointing of local state for durability. Apache I gnite provides streaming capabilities that allow data ingestion at a high scale in its in-memory data grid.\n

\n\n

\nSaikat Maitra is Lead Engineer at Target and Apache Ignite Committer and PMC Member. Prior to Ta rget\, he worked for Flipkart and AOL (America Online) to build retail and e-commerce systems. Saikat received his Master of Technology in Software Systems from BITS\, Pilani.\n

CATEGORIES:Ignite URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ignite.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:How to Slide Your Release Pass the Incubator LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJustin Mclean\n\n

\nAll podling releases need to be voted on by the incubator PMC before being rel eased to the world. I'll go through what the incubator PMC looks for in ev ery release and what you can do to make it pass that IPMC vote and get you r project one step closer to graduation. More importantly I'll cover where you can get help if you need it. In this talk\, I'll describe current inc ubator and ASF policy\, recent changes that you may not be aware of\, and go into detail the legal requirements of common open source licenses and t he best way to assemble your NOTICE and LICENSE files. Where possible I de scribe the reasons behind why things are done a certain which may not alwa ys be obvious from our documentation. I'll show how I review a release and the simple tools I use. I'll go through a worked example or two\, includi ng a fictional project called Apache Wombat\, and cover common mistakes I' ve seen in releases. \n

\n\n

\nJustin Mclean has more than 25 yea rs’ experience in developing web-based applications and is heavily involve d in open source hardware and software. He runs his own consulting company Class Software and has spoken at numerous conferences in Australia and ov erseas. In his free time\, he's active in several Apache Software Foundati on projects\, including the Apache Incubator\, and is a mentor for a numbe r of their projects. He's currently the chair of the Apache Incubator and on the ASF board. He also teaches at an online college and runs the IoT me etup in Sydney. \n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Apache IoTDB: Growing a bilingual community LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJulian Feinauer\n\n

\nOp en Source in general but also the ASF gets more and attention in Asia and especially in China. Many projects with initial chinese communities joined the incubator in the last years. This is a very positive development but over the last years we experienced that chinese communities often have dif ferent needs and challenges when learning to adopt to the Apache Way. One very important example is the language barrier which is present as many ch inese developers are not fluent in english or not as fluent as most develo pers from western countries. We feel that the IoTDB community was really s uccessful in adopting the Apache Way and in this talk we want to share our approaches and our learnings.\n

\n\n

\nJulian Feinauer joined th e IoTDB Community as one of the first external Contributors.\n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-T1755@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Past\, now and future about Apache YuniKorn (incubating): Cloud-Na tive resource scheduler LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nWilfred Spiegelenburg\nWangda Tan\n \n

\nApache YuniKorn (Incubating) is a light-weight\, universal resource scheduler for container orchestrator systems. It was created to achieve fine-grained resource sharing for various workloads efficiently on a large scale\, multi-tenant\, and cloud-native environment. YuniKorn bri ngs a unified\, cross-platform\, scheduling experience for mixed workloads that consist of stateless batch workloads and stateful services. YuniKorn now supports K8s and can be deployed as a custom K8s scheduler. YuniKorn' s architecture design also allows adding different shim-layer and adapt to different ResourceManager implementation including Apache Hadoop YARN\, o r any other systems. For this talk\, we will talk about gaps in resource s cheduling in Cloud-Native environment\, and how YuniKorn can support runni ng big data applications (like Spark/Flink/Tensorflow\, etc.) on K8s. We w ill talk about existing and upcoming features of YuniKorn (including hiera rchical of queues\, resource fairness\, gang scheduling support\, integrat ion with K8s features\, quota management\, autoscaling\, etc.). We will al so share how YuniKorn being used in community partners such as Alibaba\, C loudera\, Lyft. \n

\n\n

\nWilfred is a Staff Software Engineer fr om Cloudera in Australia. He’s also PMC member of Apache YuniKorn (incubat ing)\, Apache Hadoop committer. He has worked on Hadoop for 6 years mainly on YARN\, MapReduce\, and Spark. Before Cloudera\, he has worked for SUN Microsystems and Oracle as part of the Identity Management teams as a deve loper and consultant for over 10 years. Wilfred started his career as a le cturer at the Amsterdam University of Applied Science\; teaching\, designi ng\, and implementing multiple IT systems. Wilfred holds a Master's in Dec ision Support Systems from Sunderland University.\n\nWangda Tan is Sr. Man ager of Compute Platform engineering team @ Cloudera\, responsible for all engineering efforts related to Kubernetes\, Apache Hadoop YARN\, Resource Scheduling\, and internal container cloud. In the open-source world\, he' s a member of Apache Software Foundation (ASF)\, PMC Chair of Apache Subma rine project\, He is also project management committee (PMC) members of Ap ache Hadoop\, Apache YuniKorn (incubating). Before joining Cloudera\, he l eads High-performance-computing on Hadoop related work in EMC/Pivotal. Bef ore that\, he worked in Alibaba Cloud and participated in the development of a distributed machine learning platform (later became ODPS XLIB). \n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#T1755 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Apache Superset - A data visualization platform LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMaxime Beauchemin\n\n

\n This talk explores Apache Superset through a live demo\, and provides a hi gh level understanding of what it offers as a product. We'll also share ou r journey thus far\, and explore what it takes to grow an open source proj ect\, a community and a movement. We'll look at a retrospective of the des ign decisions\, technology choices and engineering challenges that have sh aped Superset. We'll also take a deep look into the current challenges the community is currently facing\, and peak at what is ahead.\n

\n\n

\nMax Beauchemin has worked at the leading edge of data and analytics hi s entire career\, helping shape the discipline in influential roles at dat a-dependent companies like Facebook\, Airbnb\, Lyft and Yahoo!. A leader i n the open-source community\, Max is the creator of Apache Airflow\, an op en-source tool for orchestrating complex computational workflows and data processing pipelines and Apache Superset\, a popular open-source data visu alization\, exploration and analytics platform. More recently he founded P reset\, a company devoted to building upon Superset to offer next generati on analytics as a service.\n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W0900@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:Apache Incubator\, & How incubator communities are built ? (in Hin di language) [ALC Indore] LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAditya Sharma\n\n

\nThis talk will be part of Track prepared by ALC Indore\, and *language for the talk will be Hindi*. The talk will include the details on Apache Incubato r\, how to it works\, and the important topic will be how to build a commu nity around your incubating project. ## What is Apache Incubator? Apache I ncubator is the gateway for open-source projects intended to become fully- fledged Apache Software Foundation projects. -- History -- Incubation proc ess -- Current incubating projects -- How to contribute to the incubating project? ## How to build a community for your incubating project? The comm unity is core for the success of any open source project\, in this topic I will share some tips which can help you Incubating project to grow its co mmunity. After all\, it is Community over Code. \n

\n\n

\nProjec t Management Committee (PMC) member at Apache OFBiz and Apache Roller\, Ap ache Local Community (ALC) Indore Chapter Lead\n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W0900 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W0940@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T094000Z DTEND:20200930T102000Z SUMMARY:Apache APISIX: How to implement plugin orchestration in API Gatewa yEdit proposal LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMing Wen\n\n

\nApache AP ISIX is a cloud-native API gateway that provides the same plugin mechanism as other gateways. However\, in Apache APISIX\, plugin orchestration is a lso provided that allows users to control the conditions and order for run ning plugins. Apache APISIX uses DAG(Directed Acyclic Graph) to implement this feature. In this share\, we'll introduce Apache APISIX\, and use a fe w examples to explain the advantages of plugin orchestration\, and the spe cific implementation. \n

\n\n

\nPPMC member of Apache APISIX CEO of ZhiLiu Technology Co.\, Ltd\, China Speaker of ApcheCon EU 2016\n< /p> CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W0940 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W1020@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T102000Z DTEND:20200930T110000Z SUMMARY:ECharts: a storyteller of visualization evolution LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nWenli Zhang\n\n

\nWith t he increasing demand for data visualization and a deeper understanding of theories\, the role of data visualization tools has changed dramatically o ver the years. Previously\, the main expectation was to help users underst and abstract data through a static chart. Later\, interactive tools were i ntroduced to help users better understand the relationships between data. Today\, another important aspect of our expectations is the ability to tel l the stories. We expect visualization tools to help users explore and thi nk about the story behind the data and get inspired or motivated to take f urther steps after reading the charts. In this sharing\, we will introduce why and how Apache ECharts (incubating) has evolved to adapt to the chang ing needs and formed a modern visualization tool as you see today. \n

\ n\n

\nWenli is a data visualization developer and PPMC of Apache ECh arts (incubating). She has open-sourced many data visualization projects o n GitHub (ID: Ovilia) and is enthusiastic about open-source community.\n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W1020 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W1615@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Hatching the Clutch - A Guide to the Apache Incubator LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDave Fisher\n\n

\nPodlin gs are said to be part of the Clutch. On a daily basis the status of podli ngs is evaluated from available information. This talk will describe the c lutch evaluation process and how that feeds into the Incubator website. Ad ditional topics: Whimsy and Podling information. Bootstrapping a Podling. Updating podling status. \n

\n\n

\nApache Software Foundation Mem ber and Incubator Mentor.\n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Advice to Incubator Mentors LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJustin Mclean\n\n

\nSo y ou signed up to become a mentor for a project? Do you know what it entails or what is expected of you? Your project is relying on you to help guild it to graduation. In this talk\, I'll be giving an overview of the ASF inc ubation process\, the pitfalls to watch out for\, and how projects become successful. I'll focus on everyday situations and challenges that podlings face and what's a good way to deal for mentors to deal with them. This ta lk is is for anyone who is thinking of being a mentor\, is currently a men tor or for projects wanting a smooth path to graduation. \n

\n\n

\nJustin Mclean has more than 25 years’ experience in developing web-based applications and is heavily involved in open source hardware and software . He runs his own consulting company Class Software and has spoken at nume rous conferences in Australia and overseas. In his free time\, he's active in several Apache Software Foundation projects\, including the Apache Inc ubator\, and is a mentor for a number of their projects. He's currently th e chair of the Apache Incubator and on the ASF board. He also teaches at a n online college and runs the IoT meetup in Sydney. \n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Daffodil - Kill the Data Format Problem LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Beckerle\n\n

\nD affodil is an incubator project. Its goal is killing the data format probl em by providing an implementation of DFDL (Data Format Description Languag e - an emerging standard from the Open Grid Forum) that we can all use and extend\, and integrate into all our data-consuming frameworks and applica tions. This talk will use numerous compelling examples of Daffodil parsing \, and unparsing (reconstructing) data in a variety of data formats - text ual and binary\, industry standard formats\, and ad-hoc one-of-a-kind form ats as well\, and using both XML and JSON to make the data tangible and vi sible. \n

\n\n

\nApache commiter since 2017. Currently applying k nowledge of scalable computing systems and data format issues at Owl Cyber Defense Solutions (formerly Tresys Technology). Since 2002\, Co-chair DFD L Workgroup of Open Grid Forum - working towards a standard for data forma t description so we can all stop solving this problem over and over again. Former life as a CTO of a few small/startup companies. Likes to program i n Scala and to do data archeology to figure out data from just the bits. \ n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-incubator-W1935@apachecon.com SEQUENCE:0 DTSTAMP:20200921T175745Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Teaclave: A Universal Secure Computing Platform LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMingshen Sun\n\n\n

\nApa che Teaclave (incubating) is a universal secure computing platform to make computation on privacy-sensitive data safe and secure. The platform adopt s multiple security technologies to enable secure computing\, in particula r\, Teaclave uses Intel SGX to serve the most security-sensitive tasks wit h hardware-based isolation\, memory encryption and attestation. Teaclave i s provided as a function-as-a-service platform and with many built-in func tions\, it supports a wide variety of tasks on sensitive data\, such as pr ivacy preserving machine learning\, private set intersection\, and cryptog raphic computation. More importantly\, unlike traditional FaaS\, Teaclave supports both general secure computing tasks and flexible single- and mult i-party secure computation. Last but not least\, Teaclave is written in Ru st to prevent memory-safety issues. Teaclave entered the Apache Incubator in August 2019. In this talk\, we would like to introduce this project to the whole community for the first time. We will discuss some motivation an d background of the secure computing ecosystem. Then\, we will present hig hlights of the Teaclave platform and its internal design\, talk about the roadmap of incubating and current progress\, and finally\, introduce curre nt status of Teaclave community. \n

\n\n

\nMingshen Sun works at Baidu and is a member of Apache Teaclave (incubating) PPMC (Podling Projec t Management Committee). He leads\, maintains and actively contributes to several open source projects including Teaclave\, MesaPy\, Rust OP-TEE Tru stZone SDK\, etc. Please visit his homepage (https://mssun.me) for more in formation.\n\n

CATEGORIES:Incubator URL;VALUE=URI:https://apachecon.com/acah2020/tracks/incubator.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:IndustryFusion: The democratization of Industry 4.0 LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKonstantin Kernschmidt\, Matt Mikul ina\, Marcel Wagner\n\n

\nSmall and mediumsized machine manufac turers (SMEs) currently are undergoing a tremendous transformation process . For decades high quality machines were the main development focus and pr ecision on the scale of a human hair made the engineers' hearts beat faste r. However\, growing global competition\, changed customer requirements an d the wish to extend innovation leadership require that additional data-dr iven services are offered to the cutsomers in addition to selling the mach ines. Implementing an IIoT-solution for their machines confronts the compa nies with two major challenges: 1. SMEs do not have the ressources to „exp eriment“ with proprietary IIoT-solutions\, pushing them into an undesirabl e vendor lock-in. 2. The solution has to be interoperable with the solutio ns from other manufacturers\, as usually up to 100 different machines are present in a factory and the customers only want to have one transparent S mart Factory and not 100 different digital solutions. In order to achieve an IIoT-solution that fits these needs of SMEs\, a growing group of innova tive machine manufacturers teamed up with IT- and Open-Source experts to i mplement IndustryFusion\, a cross-manufacturer interoperable open source s olution for Industry 4.0. IndustryFusion is an Apache 2.0 licensed\, fully deployable End-2-End IIoT-solution covering all required layers - i.e. pe rception\, network\, middleware\, application - for implementing a digital ecosystem. The architecture intergrates several Apache projects(PLC4X\, K afka\, Cassandra\, Beam\, Flink) and can either run entirley on premises\, using StarlingX\, or be deployed in any cloud environment.\n

\n\n

\nKonstantin Kernschmidt:
\nKonstantin Kernschmidt is passionate ab out the digital transformation of small and medium-sized enterprises. He h as a broad experience in mechanical engineering\, IT and smart factory sol utions. Konstantin is head of Research & Development / Industry 4.0 at Mic roStep Europa GmbH and the technical lead of IndustryFusion. Prior to his current position\, he was general manager of a cross-disciplinary research center focusing on innovation processes and new business models in the co ntext of Industry 4.0. He holds a PhD in automation and information system s as well as a diploma in mechanical engineering and management from the T echnical University of Munich (TUM).
\nMatt Mikulina:
\nAt Micro Step Europa - a manufacturer of high-end CNC cutting systems - he accompan ied the digital transformation of key business processes. In his new role within the IndustryFusion Team\, besides the brand communication\, he pass ionately takes care of the user experience & application design\, streamli ning processes and building a scalable solution.
\nMarcel Wagner:
\nMarcel Wagner is Software Application Engineer in Intel's IoT Group. I n this role\, he works with cusomters on Open Source Edge-Cloud platforms and Cloud Native architectures\, with focus on Industrial IoT. He contribu ted to open source projects like StarlingX\, the OpenStack open source Edg e-Cloud\, and Open IoT Service Platform\, an open source cloud platform wh ich is based on Apache projects like Kafka\, Beam\, Flink\, and Casssandra . Before joining Intel\, Marcel was researching at Siemens Corporate Techn ology and Nokia Networks on video transmission protocols and distributes a pplications. Marcel holds a Dr. rer. nat from the University of Freiburg\, Germany\, and a master of science (Dipl. Inform.) from the Karlsruhe Inst itute of Technology.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Apache StreamPipes – Flexible Industrial IoT Management LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPatrick Wiener\n\n

\nEme rging data-driven use cases in the manufacturing business often require co ntinuous integration and analysis of sensor data to identify time-critical situations. Apache StreamPipes is a new project in the Apache Incubator w hich aims at providing a self-service industrial IoT toolbox to enable non -technical users to connect\, analyze and explore IoT data streams. It pro vides many connectors for industrial communication protocols and a library of reusable algorithms to analyze sensor measurements or camera images ba sed on simple rules up to machine learning methods. A variety of data sink s allow for easy exchange with third party systems\, including many Apache IoT and Big Data projects (including Apache PLC4X\, Apache Kafka\, Apache IoTDB). In this talk\, we give an overview of Apache StreamPipes (incubat ing) and interactively show how to extend the IoT toolbox and create a cus tom data processor using the integrated Software Development Kit.\n

\n\ n

\nPatrick Wiener currently works at the FZI Research Center for In formation Technology in Karlsruhe. His research interests include Distribu ted Computing (Cloud\, Edge/Fog Computing)\, IoT\, and Stream Processing. Patrick is an expert for infrastructure management such as containers and container orchestration frameworks. He has worked in several public-funded research projects related to Big Data Management and Stream Processing in domains such as manufacturing\, logistics and geographical information sy stems.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Analyzing IIoT data with PLC4X and StreamPipes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPhilipp Zehnder\, Christofer Dutz\n \n

\nThe adoption of the Industrial Internet of Things (IIoT) i n manufacturing companies is constantly increasing. Apache software and ot her open source efforts play a key role in creating value from such data\, from connecting machines to processing streaming data and storing it in d atabases. There are several successful projects within the Apache Foundati on that can be used as building blocks to create a tailor-made IIoT soluti on for your company. In this presentation\, we will show how Apache Stream Pipes (incubating) can be used as a solution that already provides a flexi ble infrastructure for IIoT data analytics. It is an “out of the box” solu tion\, consisting of several microservices\, which allow domain experts to easily analyze data streams. Therefore\, it is closely integrated with se veral other Apache projects\, e.g. PLC4X\, Flink or IoTDB. We present how we have implemented this integration and show the advantages of the cooper ation of different Apache projects. The aim of our demonstration is to det ect faulty parts on the basis of sensor values. First\, we show how to rea lize machine connectivity with Apache PLC4X. Then we pre-process data with StreamPipes pipelines and store it in a time-series database (Apache IoTD B). After that\, we introduce how domain knowledge can be used to define a rule for classifying parts to detect quality deviations. In addition\, fo r cases where a simple rule cannot be defined\, we will use a machine lear ning model\, trained on the previously collected data.\n

\n\n

\nP hilipp Zehnder:
\nPhilipp Zehnder is a research scientist at the FZI Research Center of Information Technology. His current research interests are in the areas of Distributed Stream Processing and Streaming Machine Le arning. He is very interested in open source software\, especially in the field of IIoT\, and is involved in the Apache StreamPipes (incubation) pro ject.
\nChristofer Dutz:
\nFull blooded Apache and Open-Source e nthusiast. Invests all of his work and private time in multiple Apache Pro jects. Deeply interested in the IoT Area he is currently VP of the Apache PLC4X project and deeply involved in Apache Edgent (incubator) as well as mentor to the Apache IoTDB (incubating) podling.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Using the Mm FLaNK Stack for Edge AI (Apache MXNet\, Apache Flink\ , Apache NiFi\, Apache Kafka\, Apache Kudu) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTimothy Spann\n\n

\nToda y\, data is being generated from devices and containers living at the edge of networks\, clouds and data centers. We need to run business logic\, an alytics and deep learning at the edge before we start our real-time stream ing flows. Fortunately using the all Apache Mm FLaNK stack we can do this with ease! Streaming AI Powered Analytics From the Edge to the Data Center is now a simple use case. With MiNiFi we can ingest the data\, do data ch ecks\, cleansing\, run machine learning and deep learning models and route our data in real-time to Apache NiFi and/or Apache Kafka for further tran sformations and processing. Apache Flink will provide our advanced streami ng capabilities fed real-time via Apache Kafka topics. Apache MXNet models will run both at the edge and in our data centers via Apache NiFi and MiN iFi. Our final data will be stored in Apache Kudu via Apache NiFi for fina l SQL analytics. We can now solve IoT problems with a scalable all Apache solution that incorporates real-time streaming\, analytics and AI. Tools A pache Flink\, Apache Kafka\, Apache NiFi\, MiNiFi\, Apache MXNet\, Apache Kudu\, Apache Impala\, Apache HDFS References https://www.datainmotion.dev /2019/08/rapid-iot-development-with-cloudera.html https://www.datainmotion .dev/2019/09/powering-edge-ai-for-sensor-reading.html https://www.datainmo tion.dev/2019/05/dataworks-summit-dc-2019-report.html https://www.datainmo tion.dev/2019/03/using-raspberry-pi-3b-with-apache-nifi.html\n

\n\n

< em>\nTim Spann is a Field Engineer at Cloudera in the Data in Motion Team where he works with Apache NiFi\, MiniFi\, Kafka\, Kafka Streams\, Edge Fl ow Manager\, MXNet\, TensorFlow\, Apache Spark\, Big Data\, IoT\, Cloud\, Machine Learning\, and Deep Learning. Tim has over a decade of experience with the IoT\, big data\, distributed computing\, streaming technologies\, and Java programming. Previously\, he was a senior solutions architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone\, wh ere he is the Big Data Zone leader\, and runs a popular meetup in Princeto n on big data\, IoT\, deep learning\, streaming\, NiFi\, blockchain\, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion\, Strat a\, ApacheCon\, Data Works Summit Berlin\, DataWorks Summit Sydney\, DataW orks Summit DC\, DataWorks Summit Barcelona and Oracle Code NYC. He holds a BS and MS in computer science.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Use cases and optimizations of IoTDB LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJialin Qiao\n\n

\nApache IoTDB is a high performance database for time-series data management on t he edge and cloud for Internet of Things. This talk will introduce some us e cases of IoTDB\, including Meteorological station data management\, Subw ay data management and power plants monitoring applications. The read/writ e performance optimization and database tunning are also involved.\n

\n \n

\nPh.D student of school of software\, Tsinghua University. Exper t in IoTDB's storage engine\, query engine and application implementation on IoTDB.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-W1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:How to Become an IoT Developer (and Have Fun!) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJustin Mclean\n\n

\nI st arted off my life as a developer writing machine code and C and working on some low-level hardware projects. Then this thing called the internet com e along and I moved into the web application space for a couple of decades . More recently I've moved back into commercial IoT development and not un expectedly a lot has changed over that time. In this talk\, I'll cover wha t it's like developing IoT projects. I'll go over the tools you need and t he protocols you need to be familiar with. I'll look at how the C language has evolved to what it is today and how to write code that works well on memory constrained devices. I'll go over producing prototypes\, rapid deve lopment\, debugging and testing embedded applications and what and how muc h electronics you should learn. In short\, everything you need to know in becoming an IoT developer and have fun doing it.\n

\n\n

\nJustin Mclean has more than 25 years’ experience in developing web-based applicat ions and is heavily involved in open source hardware and software. He runs his own consulting company Class Software and has spoken at numerous conf erences in Australia and overseas. In his free time\, he's active in sever al Apache Software Foundation projects\, including the Apache Incubator\, and is a mentor for a number of their projects. He is also current the cha ir of the Apache Incubator and on the ASF board. He also teaches at an onl ine college and runs the IoT meetup in Sydney.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Home automation with Apache LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nChristofer Dutz\n\n

\nEv en if Apache PLC4X was initiated in order to communicate with industrial h ardware\, in the last years it has grown to also allow communication with building and home-automation systems. In this talk I'd like to demonstrate how I use Apache PLC4X to communicate with the KNX\, Modbus and Luxtronic 2 drivers to talk to my house and how easy it is to store this data in Apa che IoTDB (Incubating) to process it with Apache Camel\, Apache NiFi\, Apa che Edgent (Incubating) (RIP) or others and to create a Frontend using Apa che Royale (perhaps even with some nifty Apache ECharts (incubating) diagr ams).\n

\n\n

\nFull blooded Apache Member\, who likes to think ou t of the box. If others say something's impossible\, that's when Chris sta rts to become interested. He's involved in numerous Apache and even more n on-Apache projects and currently serving as the VP of Apache PLC4X which h e had the pleasure and the luck to write the first lines of code for (ok . .. and a \"few\" after that).\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-W1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Apache PLC4X or: How I Learned to Stop Worrying and Love the Indus trial IoT LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJulian Feinauer\n\n

\nTh e Apache PLC4X project left the incubator last year and is one of the youn ger projets of the ASF. It is a set of libraries for communicating with in dustrial programmable logic controllers (PLCs) using a variety of protocol s but with a shared API. At pragmatic minds we had the first (known) produ ctive deployments of PLC4X in industrial projects. As many may know\, ther e still is a gap between the very IT affine Open Source world and the OT o r shop floor world in the industry. So\, at the beginning\, these projects sometimes felt like Alices adventures when she fell down the rabbit hole. During these projects we entered a world that is completely different\, s ometimes strange but very exciting.\n

\n\n

\nJulian Feinauer stud ied mathematics at the university of Stuttgart and received his PhD in mat hematics at Ulm University. Besides his interest in open source and big da ta he had many contacts with timeseries data\, storage and evaluation. In 2016 he founded the company pragmatic industries GmbH with focus on indust rial iot and industry data processing.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T213949Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Solving IoT and Edge connectivity with Apache projects LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDejan Bosanac\, Hugo Guerrero\n\n

\nIoT and Edge solutions are all about connecting distributed sy stems together. But different use cases need different kinds of communicat ion technologies. Luckily\, Apache Software Foundation hosts multiple proj ects in this domain that can solve even the most challenging problems. Bon us point? They work great together as well\, providing a great foundation layer for all your needs. In this session we'll discuss common communicati on patterns and where they fit IoT and Edge solutions. We'll dig into the Apache projects that enable them\, such as Kafka\, Qpid dispatch router an d ActiveMQ. We'll discuss the differences and show where different approac hes make the most sense. Finally\, we'll explore how these projects can wo rk together and provide a foundation layer for a wider ecosystem targeting specifically IoT and Edge use cases. We'll give a brief architecture of E clipse Hono\, EnMasse\, Strimzi and Skupper projects. All based on Apache technologies. We'll see their benefits and place in the wider cloud IoT an d Edge ecosystems.\n

\n\n

\nDejan Bosanac:
\nDejan Bosanac i s an engineer at Red Hat with broad expertise in messaging and integration technologies. He’s been an active member of open source communities for m any years and a contributor to various projects. His latest interests revo lve around open source IoT cloud and Edge computing solutions.
\nHugo Guerrero:
\nHugo Guerrero works at Red Hat as an APIs and messaging developer advocate. In this role\, he helps the marketing team with techni cal overview and support to create\, edit\, and curate product content sha red with the community through webinars\, conferences\, and other activiti es. With more than 15 years of experience as a developer\, consultant\, ar chitect\, and software development manager\, he also works on open source software with major private and federal public sector clients in Latin Ame rica.\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-iot-W1855@apachecon.com SEQUENCE:2 DTSTAMP:20200810T213949Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Utilizing Apache NiFi and MiNiFi for EdgeAI IoT at Scale LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTimothy Spann\, Sunile Manjee\n\n

\nA hands-on deep dive on using Apache NiFi + Edge Flow Manager + MiniFi Agents with Apache MXNet\, OpenVino\, TensorFlow Lite\, and other Deep Learning Libraries on the actual edge devices including Raspberry Pi with Movidius 2\, Google Coral TPU\, NVidia Jetson Xavier\, and NVidia Je tson Nano. We run deep learning models on the edge devices and send images \, capture real-time GPS and sensor data. With our low coding IoT applicat ions providing easy edge routing\, transformation\, data acquisition and a lerting before we decide what data to stream real-time to our data space. These edge applications classify images and sensor readings real-time at t he edge and then send Deep Learning results to Apache NiFi for transformat ion\, parsing\, enrichment\, querying\, filtering and merging data to vari ous Apache data stores including Apache Kudu and Apache HBase. https://www .datainmotion.dev/2019/08/updating-machine-learning-models-at.html\n

\n \n

\nTim Spann is a Principal Field Engineer at Cloudera in the Data in Motion Team where he works with Apache NiFi\, MiniFi\, Kafka\, Kafka S treams\, Edge Flow Manager\, MXNet\, TensorFlow\, Apache Spark\, Big Data\ , IoT\, Cloud\, Machine Learning\, and Deep Learning. Tim has over a decad e of experience with the IoT\, big data\, distributed computing\, streamin g technologies\, and Java programming. Previously\, he was a senior soluti ons architect at AirisData and a senior field engineer at Pivotal. He blog s for DZone\, where he is the Big Data Zone leader\, and runs a popular me etup in Princeton on big data\, IoT\, deep learning\, streaming\, NiFi\, b lockchain\, and Spark. Tim is a frequent speaker at conferences such as Io T Fusion\, Strata\, ApacheCon\, Data Works Summit Berlin\, DataWorks Summi t Sydney\, DataWorks Summit DC\, DataWorks Summit Barcelona and Oracle Cod e NYC. He holds a BS and MS in computer science.
\nSunile: As a open source first champion in the Data of Anything space\, I have lead and enab led unreasonably successful data strategies for several premier fortune 10 0s. I simplify technical solutions through ubiquitous language for complex business challenges. Evangelism of open source adoption with a maniacal b usiness centric solutions approach earned me the Hortonworks 2016 Technica l Leadership Award. With paramount passion\, I am a business enabler who h as built from soup to nuts habitually secure\, dynamic\, scalable\, distri buted\, versatile\, and remunerative enterprise grade analytic and transac tional solutions.\n\n\n

CATEGORIES:IoT URL;VALUE=URI:https://apachecon.com/acah2020/tracks/iot.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-jena-W0940@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T094000Z DTEND:20200930T102000Z SUMMARY:Apache Jena GeoSPARQL LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMarco Neumann\n\n

\nThis presentation will discuss an implementation of GeoSPARQL for Apache Jena and a Fuseki integration. GeoSPARQL adds spatial functions to the SPARQL q uery language and enables the processing of spatial data with the popular Apache Jena project. In this presentation basic filter and property functi ons will be discussed in context of spatial relations and geometry shapes or types for the use with the Resource Description Framework (RDF) and SPA RQL. Apache Jena GeoSPARQL spatial filters can be a applied to Well-known text (WKT) representation of geometry objects and datasets using the WGS84 Geo predicates for latitude and longitude. The goal for the latest releas e of Apache Jena GeoSPARQL module was to follow generally the 11-052r4 OGC GeoSPARQL standard where possible while providing an easy to use extensio n for Apache Jena users.\n

\n\n

\nMarco Neumann is an Information Scientist with keen interest in distributed information syndication and c ontexts for the Semantic Web\, dynamic schema evolution in structured data \, information visualisation\, ontology based knowledge management\, reput ation based ranking in Semantic Social Networks (augmented collaborative o nline communities such as http://www.lotico.com)\, and last but not least the Semantic GeoSpatial Web. Since 2005 Marco applies his experiencing to large-scale information management projects in international cultural heri tage institutions and the private sector.\n

CATEGORIES:Jena URL;VALUE=URI:https://apachecon.com/acah2020/tracks/jena.html#W0940 END:VEVENT BEGIN:VEVENT UID:acah2020-jena-W1735@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:XML -> JSON -> RDF : Another iteration in data format evolution LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nClaude Warren\n\n

\nStar ting with a brief history of web and micro server data serialization forma ts\, this talk looks at the advantages of using RDF as the data format for web and micros service processing. An example of the processing as perfor med in a live application is presented. The talk demonstrates how RDF proc essed by Jena can deliver a clean\, extensible data format with simple mer ge characteristics and mechanisms for reasoning.\n

\n\n

\nClaude Warren is a Senior Software Engineer with over 30 years experience. He cur rently lives in Galway\, Ireland where he works on innovative solutions to technical problems. He is also a Comitter and Project Management Committe e member on the Apache Jena project and has several small open source proj ects on Github. He has presented papers at several conferences and has sev eral papers published both in the popular IT press and in refereed journal s. He is a founding member of the Denver Mad Scientists Club and winner of the original Critter Crunch competition.\n

CATEGORIES:Jena URL;VALUE=URI:https://apachecon.com/acah2020/tracks/jena.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-jena-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:SHACL in Apache Jena LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndy Seaborne\n\n

\nRDF databases holds data in a schema-less fashion. Adding new RDF data from ne w sources does not require existing data to be reorganised or redesigned\, nor do applications using the existing data need to be changed. Informati on about what the data looks like is not part of the database and not enfo rced\, leaving it to the application to deal with data mistakes such as ba d formats\, or missing information needed by the application. This in turn makes writing applciations more cumbersome because checking data is fit f or the applications purpose needs to be performed. An approach that is gai ning ground is data shapes\; higher level descriptions of the RDF data tha t say which RDF triples are expected\, and what the format of data values is required to be. SHACL is the W3C standard for expressing data shapes fo r such validation tasks This talk will introduce the SHACL standard and sh ow how it can be used with Apache Jena.\n

\n\n

\nAndy works on in frastructure for RDF data systems. He has been an specification editor in the SPARQL standardization process at W3C for both the original SPARQL 1.0 and also SPARQL 1.1 standards. Within the Apache Jena project\, he contri butes to the query engine and SPARQL server\, ensuring that complete imple mentations of standards are available.\n

CATEGORIES:Jena URL;VALUE=URI:https://apachecon.com/acah2020/tracks/jena.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-jena-W1855@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Buddhist Digital Archives (BUDA)\, RDF and jena-text LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nChris Tomlinson\, Élie Roux\n\n

\nBUDA is s Linked Data Platform built on Jena-Fuseki using RDF an d Jena’s Lucene integration\, jena-text. The platform enables collaboratio n in digital humanities among a variety of partners and leverages RDF and IIIF to provide open-access to a vast collection of textual materials and cultural heritage metadata about these materials.\n

\n\n

\nChris Tomlinson:
\nSenior Technologist\, working with BDRC (tbrc.org) for 1 8 years developing systems for the preservation\, access and distribution of Buddhist texts and their cultural context. Developed contributions to J ena in support of the multilingual needs of BUDA.
\nÉlie Roux:
\ nProject Lead\, working with BDRC for 4 years\, with experience in open-so urce cultural preservation projects (such as Gregorian Chant score engravi ng software . Developed contributions to IIIF and other open-source activi ties for use in BUDA.\n

CATEGORIES:Jena URL;VALUE=URI:https://apachecon.com/acah2020/tracks/jena.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-jena-W1935@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Semantic Graph BoF hosted by Apache Jena LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCommunity Participation\n\ n

\nBirds of a feather meeting to discuss all things semantic graph. Who is using them? What issues have projects encountered and how have they ov ercome them? Could the Jena help in making transition to semantic graphs e asier?\n

\n\n

\n...\n

CATEGORIES:Jena URL;VALUE=URI:https://apachecon.com/acah2020/tracks/jena.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-karaf-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200903T153024Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Apache Karaf\, multi purpose runtime LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJB Onofré\n\n

\nApache K araf is a perfect runtime for the cloud supporting several kind of program ming model. While OSGi is supported for a while\, now Karaf evolved to sup port new kind of framework like CDI\, or even Spring Boot. Thanks for that \, Karaf is a perfect multi purpose and multi tenant runtime\, providing b unch of ready to use features.\n

\n\n

\nJB is ASF member\, PMC Ch air for Apache Karaf and involve in about 20 Apache projects.\n

CATEGORIES:Karaf URL;VALUE=URI:https://apachecon.com/acah2020/tracks/karaf.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-karaf-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200903T153024Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Design Resilient Microservices using Apache Karaf and CXF: practic al experience LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrei Shakirin\n\n

\nDe velopment team just has finished the last feature after months of hard wor k. Does this mean the software is production ready now? What aspects need to be considered before deployment your Microservices to the production en vironment? What should you do in emergency situations on production? How t o make your software more reliable and resilient by deployment in Cloud? W hat are the stability and resiliency patterns and anti-patterns? All these questions will be addressed in the talk. Based on practical experience\, presenter will demonstrate the best engineering practices to design resili ent software using Apache Karaf\, CXF\, Kafka\, ActiveMQ and illustrate th em with real life cases. The following topics will be covered in presentat ion: • Stability anti-patterns (chain reactions\, cascading failures\, blo cked threads) • Stability patterns (Timeouts\, Circuit Breaker\, Bulkheads \, Fail Fast\, Async) • Clustering and Load Balancing • Logging and Monito ring • Production Diagnostic • Pooling and Caching • Load and Stress Testi ng\n

\n\n

\nAndrei is a software architect in the Talend team dev eloping the open source Application Integration platform based on Apache p rojects. The areas of his interest are REST API design\, Microservices\, C loud\, resilient distributed systems\, security and agile development. And rei is PMC and committer of Apache CXF and committer of Syncope projects. He is member of OASIS S-RAMP Work Group and speaker at Java and Apache con ferences. Last speaking experience: • DOAG 2019\, Nov 2019\, Nurnberg\, De sign Production-Ready Software • Karlsruhe Entwickertag 2017\, Mai 2017\, Karlsruhe\, Microservices with OSGi • ApacheCon Europe 2016\, Nov 2016\, S eville\, Microservices with Apache Karaf and Apache CXF: Practical Experie nce • ApacheCon Europe 2015\, Oct 2015\, Budapest\, Create and Secure Your REST API with Apache CXF • ApacheCon Europe 2014\, Nov 2014\, Budapest\, Design REST Services With CXF JAX-RS Implementation: Lessons Learned • WJA X 2011\, Nov 2011\, Munich\, Apache Days\, Enabling Services with Apache C XF\n

CATEGORIES:Karaf URL;VALUE=URI:https://apachecon.com/acah2020/tracks/karaf.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-karaf-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200903T153024Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Will it blend? Java agents and OSGi LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRobert Munteanu\n\n

\nJa va agents are a little-known but extremely powerful part of the Java ecosy stem. Agents are able to transform existing classes at runtime\, allowing scenarios such as logging and monitoring\, hot reload or gathering code co verage. However\, their usage presents a number of pitfalls as well. In th is talk we will present the steps of writing a java agent from scratch\, i ndicate various common mistakes and pain points and draw conclusions on be st practices. Special care will be taken to discuss how running in an OSGi environment affects Java agents and how we can best approach integration testing in a modular environment. After this talk participants will have a better understanding of the Java instrumentation API\, how it fits in wit h OSGi runtimes and about should / should not be done with it.\n

\n\n

\nWorking as a Senior Computer Scientist in the AEM Cloud Foundation team at Adobe\, Robert Munteanu is a software developer with a passion for open source. He is a member of the Apache Software Foundation and frequen t contributor to many open source projects\, notably Apache Sling and Apac he Jackrabbit. Robert is a frequent conference speaker\, having spoken at Devoxx\, ApacheCon and EclipseCon\, amongst others.\n

CATEGORIES:Karaf URL;VALUE=URI:https://apachecon.com/acah2020/tracks/karaf.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-karaf-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200903T153024Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Netflix: Finding middle ground between monolithic and microservice architectures. LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDmitry Vasilyev\, Saeid Mirzaei\, G eorge Ye\n\n

\nThe world of business applications is evolving. Monolithic applications are being split up into smaller microservices and deployed into virtualized environments. Engineers are striving to achieve responsiveness\, resilience and elasticity at the same time improving sepa ration of concerns and deployments via CI. The tradeoffs are usually more complex operations\, harder dependency testability\, lower developer produ ctivity in some cases and cognitive overhead as well as infrastructure cos ts. We will discuss how our team at Netflix is settling in the middle betw een monolithic and microservice architectures getting the best of the two worlds. We’ll go through different phases of the application development l ifecycle from initiation to production deployment as well as we’ll talk ho w Apache Karaf enables us to achieve the aforementioned properties of the systems we build.\n

\n\n

\nDmitry Vasilyev\, Saeid Mirzaei\, Geor ge Ye\n

CATEGORIES:Karaf URL;VALUE=URI:https://apachecon.com/acah2020/tracks/karaf.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-T1445@apachecon.com SEQUENCE:1 DTSTAMP:20200929T115846Z DTSTART:20200929T144500Z DTEND:20200929T152500Z SUMMARY:Welcome! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRich Bowen\, VP Conferences\, The A pache Software Foundation\n\n

\nA quick overview of what's comi ng today\, and how to make the most of the\nevent.\n

\n\n

\n\n

CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#T1445 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-T1500@apachecon.com SEQUENCE:0 DTSTAMP:20200928T121458Z DTSTART:20200929T150000Z DTEND:20200929T154000Z SUMMARY:The State of the Feather LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDavid Nalley\, President\, The Apac he Software Foundation\n\n

\nThe annual report from the Apache Software Foundation\n

\n\n

\nDavid Nalley is the current Presiden t of the Apache Software Foundation\n

\n\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#T1500 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-T1515@apachecon.com SEQUENCE:5 DTSTAMP:20200828T190548Z DTSTART:20200929T151500Z DTEND:20200929T155500Z SUMMARY:Why Build a Castle When You Can Create a Community
\nAdvancin g Satellite Data Analysis through Professional Open Source LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Thomas Huang\, NASA Jet Propulsion La boratory
\n\n

Thomas Huang is a Technical Group Supervisor for the JPL’s Data Product Generation Software group. He is also the Stra tegic Lead for Interactive Analytics for the JPL's National Space Technolo gy Applications Program Office\, the Principal Investigator on several NAS A Cloud-based big data analytic projects\, and the System Architect for th e NASA’s Sea Level Change Portal. As an expert in large-scale\, distribute d intelligent data systems\, Thomas led both planetary and earth data syst em projects. Thomas was the Project Technologist for NASA's Physical Ocean ography Distributed Active Archive Center (PO.DAAC). As an advocate for f ree and open source software\, Thomas led the open sourcing of many NASA-f unded technologies. He is the architect and founder of the Apache Science Data Analytics Platform (SDAP) as a community-driven\, Cloud-based Analyti c Center Framework. As an expert in data management and big data architect ure\, Thomas is a frequent invited speaker and panelist at various Earth a nd Space Informatics and Open Source events. He recently delivered keynote addresses at the ESA’s Conference on Big Data From Space (BiDS’19) and th e Australasian eResearch Organisations (AeRO)’s Collaborative Conference o n Computational & Data Intensive Science (C3DIS 2019). Thomas is a member of the NOAA’s Data Archive and Access Requirements Working Group (DAARWG) of the NOAA’s Science Advisory Board (SAB). As an educator\, Thomas is als o a Computer Science lecturer at the California State Polytechnic Universi ty\, Pomona\, and member of its Industry Advisory Board.

\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#T1515 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-T0900@apachecon.com SEQUENCE:2 DTSTAMP:20200914T171859Z DTSTART:20200929T090000Z DTEND:20200929T094000Z SUMMARY:Apache grows in China LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSheng Wu\, Founding Engineer\, Tetr ate.io\n\n

\nIn the Apache FY2020 report\, China is on the top of the download statistics. More China initiated projects joined the incub ator\, and some of them graduated as the Apache TLP.\nSheng joined the Apa che community since 2017\, in the past 3 years\, he witnessed the growth o f the open-source culture and Apache way in China.
\nMany developers have joined the ASF as new contributors\, committers\, foundation members. Chinese enterprises and companies paid more attention to open source cont ributions\, rather than simply using the project like before.\nIn the keyn ote\, he would share the progress about China embracing the Apache culture \, and willing of enhancing the whole Apache community.\n

\n\n

\n Sheng Wu is a founding engineer at tetrate.io\, leads the observability fo r service mesh and hybrid cloud. A searcher\, evangelist\, and developer i n the observability\, distributed tracing\, and APM.\nHe is a member of th e Apache Software Foundation. Love open source software and culture. Creat ed the Apache SkyWalking project and being its VP and PMC member. Co-found er and PMC member of Apache ShardingSphere. \nAlso as a PMC member of Apac he Incubator and APISIX. He is awarded as Microsoft MVP\, Alibaba Cloud MV P\, Tencent Cloud TVP.\n

\n\n\n\n\n

Wednesday\, September 30th

CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#T0900 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-W1500@apachecon.com SEQUENCE:0 DTSTAMP:20200929T120357Z DTSTART:20200930T150000Z DTEND:20200930T154000Z SUMMARY:Welcome! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRich Bowen\, VP Conferences\, The A pache Software Foundation\n\n

\nA quick welcome message\, and h ighlights of the day.\n

\n\n

\n\n

\n\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#W1500 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-W1515@apachecon.com SEQUENCE:2 DTSTAMP:20200828T191038Z DTSTART:20200930T151500Z DTEND:20200930T155500Z SUMMARY:Camille Fournier LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Two Sigma
\n\n

\nCami lle Fournier is the head of Platform Engineering at Two Sigma\, a financia l company in New York City. Prior to joining Two Sigma she was the Chief T echnology Officer of Rent the Runway\, a transformative brand that offers unprecedented access to designer fashion\, disrupting the way millions of women get dressed.

\n

She is an open source contributor and project c ommittee member for both Apache ZooKeeper and the Dropwizard web framework . Prior to working for Rent the Runway\, Camille served as a software engi neer at Microsoft\, and most recently\, spent several years as a technical specialist at Goldman Sachs\, creating distributed systems for managing r isk analysis and firmwide infrastructure.

\n

She has a BS in Computer Science from Carnegie Mellon University and an MS in Computer Science fro m the University of Wisconsin-Madison. Camille is a well-respected voice w ithin the tech community\, speaking on a variety of topics such as enginee ring leadership\, distributed systems\, scaling teams\, and technical arch itecture. In 2017 she released her book\, \"The Manager’s Path: A Guide for Tech Leaders Navigatin g Growth and Change.\"\n

\n\n
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Thursday\, October 1st

CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#W1515 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-R1500@apachecon.com SEQUENCE:0 DTSTAMP:20200929T120357Z DTSTART:20201001T150000Z DTEND:20201001T154000Z SUMMARY:Welcome! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRich Bowen\, VP Conferences\, The A pache Software Foundation\n\n

\nA quick overview of what's comi ng today\, and how to make the most of the\nevent.\n

\n\n

\n\n

\n\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#R1500 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-R1515@apachecon.com SEQUENCE:2 DTSTAMP:20200828T191038Z DTSTART:20201001T151500Z DTEND:20201001T155500Z SUMMARY:Edmon Begoli LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:High Performance Computing with Apach e Spark and Parquet on Mission Critical Tasks\n\n

Oak Ridge Nat ional Laboratory (ORNL) is known for its deployment of some of the world's fastest supercomputers. This legacy brings us opportunities to work on so me of the most challenging societal problems. Often\, these problems requi re approaches that are more comprehensive than what specific high-performa nce computing solutions can solve. In this talk\, we will talk about the e ssential role that Apache Spark and Parquet played in solving some of thes e problems. We will illustrate Apache Spark and Parquet's uses with a cas e study related to suicide and overdose risk where prevention. The result is a 300x speedup in processing from 75+ hours for the original algorithm to 15 minutes with a new one. We will discuss specific techniques behind t his accomplishment and the lessons learned.

\n\n

Edmon Begoli\, PhD w orks at Oak Ridge National Laboratory (ORNL)\, where he leads research and development programs aimed at scaling and improving the resilience of cri tical decision making.

\n\n

Edmon is a committer with Apache Software Foundation\, and is a joint faculty professor of Computer Science at the University of Tennessee\, EECS department.

\n\n

Sponsored Keynotes\n\n

Tuesday

\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#R1515 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-T1545@apachecon.com SEQUENCE:2 DTSTAMP:20200903T153024Z DTSTART:20200929T154500Z DTEND:20200929T162500Z SUMMARY:Double inflection point: Open Source meets AI LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Sam Lightstone:\nChief Technology Off icer for AI Strategy\, IBM
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\nAbstract: Machine Learni ng is almost as old as the electronic computer\, but the domain has experi enced a massive infusion of energy and investment over the past 8 years. During this time the open source community has simultaneously developed a wide landscape of rich\, sophisticated OSS packages for machine learning a nd deep learning such as Apache Marvin-AI\, DLlab\, Spark\, MLlib\, MADlib and OpenNLP. In this talk IBM CTO for AI Strategy\, Sam Lightstone\, will explore the confluence of these two disruptions and the possibilities tha t lie ahead for dramatic advances in AI\, computation power\, distributed computing\, and a sea-change in computer science. \n\n

\nSam Lights tone is IBM Chief Technology Officer for AI Strategy\, IBM\nFellow and a M aster Inventor in the IBM Data and AI group. He is also\nchair of the Data and AI Technical Team\, the working group of IBM’s\ntechnical executives in the division. He has been the founder and\nco-founder of several large- scale initiatives including AI databases\,\nnext generation data warehousi ng\, data virtualization\, autonomic\ncomputing for data systems\, serverl ess cloud SQL query\, and cloud native\ndatabase services. He co-founded t he IEEE Data Engineering Workgroup on\nSelf-Managing Database Systems. Sam has more than 65 patents issued and\npending and has authored 4 books and over 30 papers. Sam’s books have\nbeen translated into Chinese\, Japanese and Spanish. In his spare time\nhe is an avid guitar player and fencer. His Twitter handle is\n@saml ightstone.\n

\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#T1545 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-T1600@apachecon.com SEQUENCE:3 DTSTAMP:20200903T153024Z DTSTART:20200929T160000Z DTEND:20200929T164000Z SUMMARY:DataStax Astra and Apache Cassandra: Sustainable Open Source in th e Cloud Era LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Jonathan Ellis\, Co-founder and CTO\, DataStax
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\nApache Cassandra solves database performa nce at scale better than any other system in the world\, but it was design ed for a world of self-managed infrastructure. This created a lot of roug h edges for the level of automation DataStax needed to build its Astra man aged service for Cassandra. Building Astra also exposed some gaps in Cass andra’s feature set that modern developers want and expect from a database -as-a-service.

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DataStax believes that developers and businesses shouldn’t have to give up ownership of their data to take advantage of the benefits of cloud infrastructure. We want everyone to have the freedom t o deploy anywhere\, without lock-in. This talk will explain how we’re bri nging the enhancements we made for Astra back to Apache Cassandra. Follow ing the Cassandra Enhancement Proposal process\, we are showing that cloud and open source are not mutually exclusive.\n

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\nJonathan El lis is a co-founder of DataStax. Before DataStax\, Jonathan was Project Ch air of Apache Cassandra for six years\, where he built the Cassandra proje ct and community into an open-source success. Previously\, Jonathan built an object storage system based on Reed-Solomon encoding for data backup pr ovider Mozy that scaled to petabytes of data and gigabits per second throu ghput.\n

\n\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#T1600 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-W1545@apachecon.com SEQUENCE:4 DTSTAMP:20200909T160425Z DTSTART:20200930T154500Z DTEND:20200930T162500Z SUMMARY:Rethinking Language: Why Now\, What’s Next LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:Kim Huang\, Content Strategist\, Red Hat\n\n

\nAt the core of open source is the idea that we contin ually change and adapt as we learn new information or discover better ways of doing things. We welcome ideas from anyone to help us make the best so ftware available. Adapting the language we use in our code to become more welcoming for all current and future community members is part of that eth os. Learn about why Red Hat is taking steps to rethink the language of our code and documentation\, and the impact this work will have.\n

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\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#W1545 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-W1600@apachecon.com SEQUENCE:2 DTSTAMP:20200909T170516Z DTSTART:20200930T160000Z DTEND:20200930T164000Z SUMMARY:Fostering strong\, open source communities that benefit all of us LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCatherine McGarvey\, VP Engineering \, VMWare\n\n

\nWe all desire strong open source communities\, but what does that even mean? What are health metrics that you can track a nd measure to see that you are making an impact here. Let's explore the di fferent open source communities approaches as case studies. What actions c an you take to help make your community more inclusive? \n

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\ nCatherine McGarvey is the VP of Engineering at VMware\, leading engineeri ng for developer facing communities. She has had the privilege of being in volved in a number of OS communities including Apache Geode\, RabbitMQ\, K ubernetes\, cloud foundry and knative. \n\n \n

\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#W1600 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-R1545@apachecon.com SEQUENCE:3 DTSTAMP:20200909T174449Z DTSTART:20201001T154500Z DTEND:20201001T162500Z SUMMARY:A Rising Tide Lifts All Boats: Working With Contributors of All Si zes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAnil Inamdar\, Head of US Consultin g and Delivery\, Instaclustr\n\n

\nThe Open source development model has changed significantly since its heydays in the 1990s. Today ther e are more projects\, more contributors\, additional financing and vendors of various kinds – support\, add-ons\, cloud providers. The Apache commun ity still continues to play a pivotal role in promoting open source projec ts\, setting community standards\, providing framework for arbitrations an d ensuring quality for the projects. \n

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\nParticipations from cont ributors of all sizes – individual\, company affiliated\, and vendor suppo rted act as the rising tide and help expand the open source market. The ke y however is to ensure that we contribute back to the project and the foun dation. Working together creates a rising tide lifting its participants. A s the saying goes – “If you want to go fast\, go alone\; but if you want t o go far\, go together.”\n

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\n\n CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#R1545 END:VEVENT BEGIN:VEVENT UID:acah2020-keynotes-R1600@apachecon.com SEQUENCE:1 DTSTAMP:20200914T162505Z DTSTART:20201001T160000Z DTEND:20201001T164000Z SUMMARY:The heat is on: architecting for hot analytics LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGian Merlino\,\nCTO and Co-Founder\ , Imply and Apache Druid PMC Chair\n\n

\nToday\, the industry o ffers numerous systems for the analysis of large amounts of data. Under th e hood\, they span a variety of interesting and unique architectures. In t his talk\, we'll discuss why you can never seem to find that single perfec t system\, and how to think about and evaluate the capabilities of various systems through the prism of a temperature-based spectrum of use cases\, from cold to hot analytics.\n

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CATEGORIES:Keynotes URL;VALUE=URI:https://apachecon.com/acah2020/tracks/keynotes.html#R1600 END:VEVENT BEGIN:VEVENT UID:acah2020-mahout-R1615@apachecon.com SEQUENCE:0 DTSTAMP:20200921T182718Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:A Data Scientist First-Time Mahout Experience: Tips and Takeaways (Talk in Spanish) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJose Francisco Hernandez Santa Cruz \n\n

\nEl constante incremento en la disponibilidad de la data y su crecimiento exponencial crean una oportunidad perfecta para descubrir los detalles más reveladores y predicciones más precisas que los datos pu eden entregar. Desafortunadamente\, esto viene\, a veces\, a expensas de u na alta compejidad computacional: la cada vez más grande ingesta de datos requiere un mayor poder de cómputo\, creando limitaciones en un proyecto. Una solución planteada es el empleo de computación distribuida: sistema di stribuido de computadores ejecutando tareas en paralelo. Un framework que rápidamente se volvió popular en este ámbito es Apache-Spark. Sin embargo\ , a medida que el aprendizaje automático se volvió\, no solo más popular\, pero más demandante de poder de cómputo\, Apache Mahout nos trajo un fram ework enfocado a estadistica y aprendizaje automático. Como científico de datos\, y primera vez como usuario de Apache Mahout\, mis experiencias pro veen de detalles y contenido desde un punto de vista de nuevo usuario\, qu e por primera vez experimenta con Apache Mahout\, proveyendo lecciones apr endidas especialmente para usuarios de Python con poca o ninguna experienc ia en Scala o aprendizaje distribuido.
(English Translation\, Talk wi ll be in Spanish) The constant increase of data availability and exponenti al growth makes an excellent opportunity to uncover the most revealing ins ights and most accurate predictions data can give us. Unfortunately this c omes\, sometimes\, at the expense of highly complex computation. One frame work which quickly became popular is Apache-Spark: distributed computing f or big data processing. However\, as machine learning became\, not only mo re popular\, but more demanding of distributive computation\, Apache Mahou t brought us a nice framework with statisticians and machine learning prac titioners in mind. As a data scientist for IBM and first time user of Apac he Mahout\, my experiences provide an insight from a first-time user point -of-view\, providing takeaways and lessons learned\, specially for Python and R users with no or little experience in Scala or distributed learning. \n

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\nGraduated as Industrial Engineer in the city of Lima\, Peru\, I started pursuing the data scientist career at the age of 24\, foc used in Machine Learning algorithms and Artificial Neural Networks researc h. Certified by IBM and Open Group as level 1 data scientist\, I'm current ly finishing MIT's Micromaster in Statistics and Data Science and preparin g my application for a master's program in Machine Learning. With two rese arch papers under review for publication\, and leading for one year a Mach ine Learning mentoring program at IBM\, I'm starting my giveback period\, trying to contribute to open source technology as well as the scientific c ommunity with articles published as independent researcher.\n

CATEGORIES:Mahout URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mahout.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-mahout-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200921T182718Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Modern Recommenders with Mahout LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPatrick (Pat) Ferrel\n\n\n

\nMahout in years past was known for being the place to go for premium OSS recommenders. Time passed and recommender technology moved on. With Ma hout 0.13+ Mahout is once contains a state-of-the-art modern recommender t argeting broad use. This talk covers the the 3rd generation Correlated Cro ss Occurrence Algorithm as it is implemented in Spark-based Mahout. CCO wi ll be explained via the mathematics and theory behind it as well as optimi zations made in Mahout to produce a production worthy implementation. We c all CCO a 3rd generation algorithm since it comes after Cooccurrence and M atrix Factorization and is fully multimodal\, making it possible to use ma ny indicators of user behavior as well as contextual and content or metada ta based indicators. While Mahout implements the core of the algorithm we will discuss how Mahout can be integrated into a full end-to-end data inge stion and serving architecture. We will also review some comparative perfo rmance data.\n

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\nPat has worked in startups building apps ba sed on Machine Learning since 2000. He has worked in NLP/NER\, text mining \, and recommenders. He became a committer to Apache Mahout in 2012\, and Apache PredictionIO in 2017. He is currently the Chief Consultant at the O SS and ML consultancy ActionML where he has led nesarly 100 deployments of their Harness ML Server which makes use of Apache Mahout and Apache Spark .\n

CATEGORIES:Mahout URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mahout.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-mahout-R1735@apachecon.com SEQUENCE:0 DTSTAMP:20200921T182718Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Mahout and Kubeflow Together At Last LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTrevor Grant\n\n

\nKubef low is an exciting and fashionable new platform for Data Science. In this talk we will discuss how to use Apache Mahout (and Apache Spark) on it.\n< /p>\n\n

\nSomeday he will be the Chief Mugwug. Not today\, but somed ay.\n

CATEGORIES:Mahout URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mahout.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-mahout-R1815@apachecon.com SEQUENCE:0 DTSTAMP:20200921T182718Z DTSTART:20201001T181500Z DTEND:20201001T185500Z SUMMARY:Apache Mahout on Zeppelin LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrew Musselman\n\n

\nT his talk will demonstrate adding a Mahout interpreter to the Zeppelin note book system. Zeppelin is an extensible notebook project which allows users to add interpreters which will understand and run a wide variety of code\ , ranging from Python\, to Spark-flavored Scala\, to SQL dialects\, to oth er domain-specific languages (DSLs). In our case we will add an interprete r which understands the Mahout DSL called Samsara\, which focuses on matri x math at scale. The activities in this tutorial will span: (1) Getting th e latest software releases (2) Setting environment variables (3) Creating and configuring the Samsara interpreter (4) Starting a notebook and import ing a data set (5) Doing some data manipulation and calculation (6) Produc ing some plots and charts (7) Showing some ways to publish dashboards and individual cells The audience should be prepared with an operating system which has a recent version of Java (>= jdk 1.8)\, and an installation scri pt will be provided for people who would like to set a computer up in adva nce to follow along. This talk is for anyone with an interest in data scie nce and analytics. Blog post with similar previous work/style: https://mah out.apache.org/docs/latest/tutorials/misc/mahout-in-zeppelin\n

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< em>\nAndrew Musselman runs business and data operations in North America f or 24i\, chairs the Apache Mahout Project\, and hosts the Adversarial Lear ning podcast. He loves distributed matrix math and lives in Seattle with h is wife and kids.\n

CATEGORIES:Mahout URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mahout.html#R1815 END:VEVENT BEGIN:VEVENT UID:acah2020-mahout-R1855@apachecon.com SEQUENCE:1 DTSTAMP:20200921T182718Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:The Long and Winding Road to Becoming A Mahout Committer LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTrevor Grant\, Andrew Musselman\, P at Ferrel\n\n

\nJk! We want you to be a committer. In this pane l discussion various PMC members from (past and?) present will discuss how someone who knows very little or maybe nothing about Apache can go about getting involved with our community\, what parts of the project we need he lp on\, how we operate and more. If we can get some PMC members from Mahou t of Yesteryear we will listen their stories of the Mahout of the Past. If we're really hurting for time\, AKM will freeform about the joys of being a HAM Radio operator.\n

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\nTrevor Grant:
\nTrevor is a former data scientist who has given it all up to pursue the app game\, how ever will have probably given that up to pursue some other game by the tim e the conference rolls around.
\nAndrew Musselman:
\nAndrew Muss elman runs business and data operations in North America for 24i\, chairs the Apache Mahout Project\, and hosts the Adversarial Learning podcast. He loves distributed matrix math and lives in Seattle with his wife and kids .\n

CATEGORIES:Mahout URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mahout.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-mahout-R1935@apachecon.com SEQUENCE:0 DTSTAMP:20200921T182718Z DTSTART:20201001T193500Z DTEND:20201001T201500Z SUMMARY:Mahout: State of the Matrix LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTrevor Grant\n\n

\nIn th is talk we will go over recent developments\, discuss upcoming changes\, a nd share the PMC's vision for Mahout over the next 12 months and beyond.\n

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\nPMC of Mahout\n

CATEGORIES:Mahout URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mahout.html#R1935 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-T0930@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200929T093000Z DTEND:20200929T101000Z SUMMARY:New Apache Members from China\, responsibilities and obligations LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSheng Wu\, Juan Pan\, Ning Jiang\, Duo Zhang\n\n

\nThere are 11 of 35 new ASF members from China. With more and more project initialized from China and graduated from Incub ator as new TLPs\, China has more people involved in the Apache. In this p anel\, we invited Chinese Apache Members to talk about their open source j ourney and their responsibilities and obligations for the Apache Software Foundation and open source world.\n\n

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\nSheng Wu:
\nHe is an Apache Member\, the Apache SkyWalking VP\, and a PMC member. Also be a member of Apache ShardingSphere\, APISIX\, and Incubator PMC. He mentor s several China initialized incubator project. Talked a lot about the open source in many conferences.
\nJuan Pan:
\nAs a senior DBA worke d at JD.com\, the responsibility is to develop the distributed database an d middleware\, and the automated management platform for database clusters . As a PMC of Apache ShardingSphere\, I am willing to contribute to the OS community and explore the area of distributed databases and NewSQL.
\nNing Jiang:
\nWillem Jiang is the technical expert of Huawei\, a me mber of the Apache Software Foundation\, he worked on many Apache projects like Camel\, CXF\, ServiceMix and ServiceComb. Before joining Huawei\, Wi llem was the principal engineer of RedHat working on Fuse ESB\, he also wo rked for FuseSource\, IONA and Travelsky.inc. Willem gave talks on micro-s ervices\, distributed systems and open source in several conferences\, lik e QCon Beijing\, ArchSummit etc.
\nDuo Zhang:
\nDuo Zhang is a p rincipal software engineer at Xiaomi\, works for the cloud platform depart ment. He is a member of the Apache Software Foundation\, and also the chai r of the Apache HBase PMC. Besides HBase\, he also works on several other Apache projects like Hadoop\, Yetus\, etc. He is a mentor of several Apach e incubator projects such as NuttX and Pegasus.\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#T0930 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-T1010@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200929T101000Z DTEND:20200929T105000Z SUMMARY:From Web Engineer to Apache APISIX PMC LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nZhiyuan Ju\n\n

\nThe ope n source project Apache HTTP Server\, carry the data connectivity between many terminals. Without the help of open source projects\, today's Interne t will be much inferior. Therefore\, we encourage developers to actively p articipate in open source projects in order to better maintain the communi ty ecology. In this meeting\, I will share my experience from a Web engine er to continuous participation in open source projects\, as well as the cu ltural differences between the Apache community and others\, so that more developers can understand\, embrace and participate in open source project s.\n

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\nPMC member of Apache APISIX The core member of freeCo deCamp China\, an organization involving to help people to learn web techn ologies Web and Security are also my favorites.\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#T1010 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-T1050@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200929T105000Z DTEND:20200929T113000Z SUMMARY:New Features of Apache CarbonData 2.0 LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCai Qiang\n\n

\nApache C arbonData is an indexed columnar data format for fast analytics on big dat a platform. The latest version 2.0 is a milestone version. Compared with t he 1.x version\, the data loading and index capabilities are greatly impro ved. The CDC capability is improved to support the update\, delete\, and m erge functions. The reconstructed MV supports multiple formats.\n\n

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\nCai Qiang\, Apache CarbonData PMC\, Committer\, more 10 years co de experience in big data domain\, has deep understanding for Hadoop\, Spa rk\, Hive etc. As CarbonData’s initial member\, who was responsible for co re architecture design of data loading and index features.\n\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#T1050 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-T1130@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200929T113000Z DTEND:20200929T121000Z SUMMARY:ECharts: could the customization be both easy and highly personali zed? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShuang Su\n\n

\nThe majo r task of a charting library is to find out some appropriate ways to abstr act the data visualization programing. Usually\, common cases\, easy-to-us e\, \"flexibility\" and \"maintainability\" should be considered to come u p with some concepts and API for users to learn and express their requirem ents. In this designing\, is it possible to both satisfy the easy-to-use a nd highly personalized? This topic will share the understanding of these a bstraction in the evolution of echarts program\, and illustrate the cases that benefited from the concepts like \"custom series\"\, \"series/coordin ate system combination\".\n\n

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\nApache ECharts (incubating) PPMC member\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#T1130 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-T1210@apachecon.com SEQUENCE:1 DTSTAMP:20200903T153024Z DTSTART:20200929T121000Z DTEND:20200929T125000Z SUMMARY:New Feature of Apache ShardingSphere 5.x LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nLiang Zhang\n\n

\nThe fi rst version of Apache ShardingSphere 5.x will be released soon. In version 5.x\, Apache ShardingSphere has made significant innovations from archite cture design to product scope. Apache ShardingSphere 5.x follow pluggable architecture design concept to build a flexible\, embeddable and extensibl e project. Apache ShardingSphere 5. X no longer takes data sharding as ker nel\, but turns to building distributed database ecosystem. In the new ver sion\, core functions such as data sharding\, distributed transaction and database governance are completely separated from the kernel and become a part of its pluggable component. Through SPI\, the ecosystem is fully open ed\, and the functions of data migration\, elastic scheduling\, data encry ption\, shadow table are fully integrated into the product ecology. This p resentation will comprehensively introduce the new features of Apache Shar dingSphere 5.x.\n

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\nLiang Zhang\, Architecture expert of Tec hnical Center\, JD Digital Technology(JD.com)\, Apache ShardingSphere PMC Chair. Passionate to open source\, and advocate clean code. He recently fo cuses on building distributed database middleware Apache ShardingSphere as the first-rate data solution in the finance industry. Liang Zhang has pub lished a book named \"Future Architecture: from SOA to Cloud Native\" on M arch\, 2019. GitHub: https://github.com/terrymanu\, communications are alw ays welcomed.\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#T1210 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-W0900@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:How does Apache Dolphin Scheduler (Incubator) support 100\,000-lev el data task scheduling? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nLidong Dai\n\n

\nFirst I will introduce the development of the DolphinScheduler community\, and th en introduce why we had to reinvent the wheel to rebuild the scheduling of big data tasks\, the overall design ideas of DolphinScheduler\, considera tions\, and the features and capabilities of DolphinScheduler. Next\, I wi ll introduce evolution process of DolphinScheduler architecture. In this s hare\, I will also talk about the challenges and accumulated experience we have encountered in the scheduling of big data tasks. then\, I will share some user cases and usage scenarios. Finally\, I will share the history o f open source.\n

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\nHe is currently the director of Analysys Big Data Platform & Apache DolphinScheduler PPMC\, responsible for the dat a process architecture\, technology selection\, and technical breakthrough s of the daily 30 billion-level data processing chain. Focusing on the res earch and development of data platform architecture for 10 years\, he good at data platform construction\, cluster performance tuning\, and data war ehouse construction. He has served as a data architect for many big data c ompanies and has some experience in retail business\, olap data analysis\, and mining.\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#W0900 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-W0940@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200930T094000Z DTEND:20200930T102000Z SUMMARY:OSS.Chat - A bridge to the Apache Way in China LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nHuan\n\n

\nThe mission o f the OSS.Chat project is to bridge the three-way communication and transl ation barriers between WeChat and other social platforms (future) and GitH ub Issues and mailing lists to the open source development community\, mak ing ASF's cultural\, technical\, and collaborative processes acceptable qu ickly and easily\, rather than stumbling from the start. With Chatbot\, an automated process mechanism\, developers can more easily share and commun icate information about the development of open source projects. In partic ular\, the archiving and secondary induction of open information to the co mmunity is one of the things that we think is very meaningful. Through OSS .Chat project\, we hope to further promote\, disseminate\, and even optimi ze the culture\, technology\, and collaboration of the Apache project comm unity.\n

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\nHuan\, PreAngel Partner\, Author of Wechaty\, an Angel Investor\, Serial Entrepreneur\, Machine Learning PhD Student\, Micr osoft AI MVP\, Google ML GDE\, Tencent Chatbot TVP\, Conversational AI Cod er with passion\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#W0940 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-W1020@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200930T102000Z DTEND:20200930T110000Z SUMMARY:Apache TubeMQ: a new choice of MQ in big data scenarios LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nGosonzhang\n\n

\n\nThis paper introduces the challenges faced by Message Queue (MQ) when data tran smission changes from 10 billion to trillions in the big data scenario\, a nd how TubeMQ solves such problems to meet business needs.\n

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\nTubeMQ project PPMC member\, working in the data storage group of Tence nt Data Platform Department.\n\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#W1020 END:VEVENT BEGIN:VEVENT UID:acah2020-mandarin-W1100@apachecon.com SEQUENCE:0 DTSTAMP:20200903T153024Z DTSTART:20200930T110000Z DTEND:20200930T114000Z SUMMARY:Apache Doris - A fast MPP database for all modern analytics on big data LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMingyu Chen\n\n

\nDoris is an analytical database project that entered the Apache incubator in 201 8. The design goal of Doris is to provide users with an interactive analys is system that responds to massive amounts of data in sub-second levels th rough an elegant and simple system architecture\, effectively supporting r eal-time data analysis. Doris's distributed architecture is very simple\, easy to operate and maintain\, and can support very large data sets of mor e than 10PB. Doris can also meet a variety of data analysis needs\, includ ing history data reports\, real-time data analysis\, interactive data anal ysis\, and exploratory data analysis. Make data analysis easier. The speec h mainly introduced the development history of Doris\, architecture design \, key features and classic use cases.\n

\n\n

\nBaidu senior R&D engineer\, Apache Doris(incubating) PPMC\, Bachelor of University of Scien ce and Technology of China\, Master of Institute of Computing Technology\, Chinese Academy of Sciences\, 6 years of big data research and developmen t experience.\n\n

CATEGORIES:Mandarin URL;VALUE=URI:https://apachecon.com/acah2020/tracks/mandarin.html#W1100 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:TVM: An End to End Deep Learning Compiler Stack LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTianqi Chen\n\n

\nApache (incubating) TVM is an open deep learning compiler stack for CPUs\, GPUs\, and specialized accelerators. It aims to close the gap between the produc tivity-focused deep learning frameworks\, and the performance- or efficien cy-oriented hardware backends. TVM provides the following main features: - Compilation of deep learning models in Keras\, MXNet\, PyTorch\, Tensorfl ow\, CoreML\, DarkNet into minimum deployable modules on diverse hardware backends. - Infrastructure to automatic generate and optimize tensor opera tors on more backend with better performance. In this talk\, I will cover the new developments in TVM in the past year around the areas of more back end\, automation and model support.\n

\n\n

\nTianqi Chen received his PhD. from the Paul G. Allen School of Computer Science & Engineering at the University of Washington\, working with Carlos Guestrin on the inte rsection of machine learning and systems. He has created three major learn ing systems that are widely adopted: XGBoost\, TVM\, and MXNet(co-creator) . He is a recipient of the Google Ph.D. Fellowship in Machine Learning. He is currently the CTO of OctoML.\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Apache Submarine: State of the union LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nWangda Tan\, Zhankun Tang\n\n

\nApache Submarine is the ONE PLATFORM to allow Data Scientists to c reate end-to-end machine learning workflow. ONE PLATFORM means it supports Data Scientists to finish their jobs on the same platform without frequen tly switching their toolsets. From dataset exploring data pipeline creatio n\, model training (experiments)\, and push model to production (model ser ving and monitoring). All these steps can be completed within the ONE PLAT FORM. In this talk\, we’ll start with the current status of Apache Submari ne – how it is used today in deployments large and small. We'll then move on to the exciting present & future of Submarine – features that are furth er strengthening Submarine as the ONE PLATFORM for data scientists to trai n/manage machine learning models. We’ll discuss highlight of the newly rel eased 0.4.0 version\, and new features 0.5.0 release which is planned in 2 020 Q3: - New features to run model training (experiments) on K8s\, submit mode training job by using easy-to-use Python/REST API or UI. - Integrati on to Jupyter notebook\, and allows Data-Scientists to provision\, manage notebook session\, and submit offline machine learning jobs from notebooks . - Integration with Conda kernel\, Docker images to make hassle-free expe riences to manage reusable notebook/mode-training experiments within a tea m/company. - Pre-packaged Training Template for Data-Scientists to focus o n domain-specific tasks (like using DeepFM to build a CTR prediction model ). We will also share mid-term/long-term roadmap for Submarine\, including Model management for model-serving/versioning/monitoring\, etc.\n

\n\n

\nWangda Tan:
\nWangda Tan is Sr. Manager of Compute Platform engineering team @ Cloudera\, responsible for all engineering efforts rela ted to Kubernetes\, Apache Hadoop YARN\, Resource Scheduling\, and interna l container cloud. In open-source world\, he's a member of Apache Software Foundation (ASF)\, PMC Chair of Apache Submarine project\, He is also pro ject management committee (PMC) members of Apache Hadoop\, Apache YuniKorn (incubating). Before joining Cloudera\, he leads High-performance-computi ng on Hadoop related work in EMC/Pivotal. Before that\, he worked in Aliba ba Cloud and participated in the development of a distributed machine lear ning platform (later became ODPS XLIB).
\nZhankun Tang:
\nZhanku n Tang is Staff Software Engineer @Cloudera. He’s interested in big data\, cloud computing\, and operating system. Now focus on contributing new fea tures to Hadoop as well as customer engagement. Zhankun is PMC member of A pache Hadoop/Submarine\, prior to Cloudera/Hortonworks\, he works for Inte l.\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-T1735@apachecon.com SEQUENCE:0 DTSTAMP:20200929T110904Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Apache MXNet 2.0: Bridging the Gap between DL and ML LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSheng Zha\n\n

\nDeep lea rning community has largely evolved independently from the prior community of data science and machine learning community in NumPy. While most deep learning frameworks now provide NumPy-like math and array library\, they d iffer in the definition of the operations which creates a steeper learning curve of deep learning for machine learning practitioners and data scient ists. This creates a chasm not only in the skillsets of the two different communities\, but also hinders the exchange of knowledge. The next major v ersion\, 2.0\, of Apache MXNet (incubating) seeks to bridge the fragmented deep learning and machine learning ecosystem. It provides NumPy-compatibl e programming experiences and simple enhancements to NumPy for deep learni ng with the new Gluon 2.0 interface. The NumPy-compatible array API also b rings the advances in GPU acceleration\, auto-differentiation\, and high-p erformance one-click deployment to the NumPy ecosystem.\n

\n\n

\n Sheng Zha is an Applied Scientist at Amazon AI. He’s also a committer and PPMC member of Apache MXNet (Incubating)\, steering committee member of Li nux AI Foundation ONNX\, and maintainer of the GluonNLP project. In his re search\, Sheng focuses on the intersection between deep learning-based nat ural language processing and computing systems\, with the aim of enabling learning from large-scale language data and making it accessible.\n CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Streaming Machine Learning with Apache Kafka and TensorFlow (witho ut a Data Lake) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nKai Waehner\n\n

\nMachin e Learning (ML) is separated into model training and model inference. ML f rameworks typically load historical data from a data store like HDFS or S3 to train models. This talk shows how you can avoid such a data store by i ngesting streaming data directly via Apache Kafka from any source system i nto TensorFlow for model training and model inference using the capabiliti es of “TensorFlow I/O”. The talk compares this modern streaming architectu re to traditional batch and big data alternatives and explains benefits li ke the simplified architecture\, the ability of reprocessing events for tr aining different models\, and the possibility to build a scalable\, missio n-critical\, real time ML architecture with muss less headaches and proble ms\n

\n\n

\nKai Waehner is a Technology Evangelist at Confluent. He works with customers across Europe\, US\, Middle East and Asia and inte rnal teams like engineering and marketing. Kai’s main area of expertise li es within the fields of Big Data Analytics\, Machine Learning\, Hybrid Clo ud Architectures\, Event Stream Processing and Internet of Things. He is r egular speaker at international conferences such as ApacheCon and Kafka Su mmit\, writes articles for professional journals\, and shares his experien ces with new technologies on his blog: www.kai-waehner.de.\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-W1615@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Deep Learning in Java LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nQing Lan\n\n

\nAI is evo lving rapidly\, and is used widely in a variety of industries. Machine lea rning (ML) applications ranging from basic text classification to complex applications such as object detection and pose estimation are being develo ped to be used in enterprise applications. Currently\, software engineers using Java have a large barrier to entry when they try to adopt Deep Learn ing (DL) for their applications. Python being the de-facto programming lan guage for ML adds additional gradient to an already steep learning curve. This tutorial will introduce an open-source\, framework-agnostic Java libr ary — Deep Java Library (DJL) for high-performance training and inference in production. DJL supports a variety of Deep Learning engines (including but not limited to Apache MXNet\, TensorFlow and PyTorch) and provides a s imple and clean Java API that will work the same with each engine. Additio nally\, DJL offers the DJL Model Zoo - a repository of models that makes i t easy to share models across teams. This tutorial will walk software engi neers through the core features of DJL and demonstrate how it can be used to simplify experience of serving models. By the end of the session\, user s will be able to train and deploy DL models from a variety of DL framewor ks into production environments and serve user requests using Java. Websit e: https://djl.ai/\n

\n\n

\nQing is a SDE II in the AWS Deep Lear ning Toolkits team. He is one of the co-authors of DJL (djl.ai) and PPMC m ember of Apache MXNet. He graduated from Columbia University in 2017 with a MS degree in Computer Engineering and has worked on model training and i nference. Qing has presented a workshop about Apache MXNet in ApacheCon 20 19(Las Vegas) about using Java for Deep Learning inference.\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-W1655@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Running ML algorithms with ML tools available in Apache Ecosystem LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShekhar Prasad Rajak\n\n

\nIn these days\, having libraries to get abstract methods to use machine learning algorithm in the application is important but to train our model effectively in lesser time & resources\; for our own customize algorithm is more important. Machine learning technology is changing every single da y\, so let's spend time on how Researchers and Software Developers can lev erage the powerful features provided by Apache libraries & frameworks. In this talk we will focus on Apache libraries/frameworks available for distr ibuted training\, large scale & less costly data transfer during the whole Model training life cycle. Fundamentals and motive behind following Apach e Projects: * Apache Spark MLlib: Simplifies large scale machine learning pipelines\, using distributed memory-based Spark architecture. The best fo r building & experimenting new algorithms. * Apache MxNet: A lean\, flexib le\, and ultra-scalable deep learning framework that supports state of the art in deep learning models * Apache Singa: It provides intelligent datab ase system\, distributed deep learning by partitioning the model and data onto nodes in a cluster and parallelize the training. * Apache Ignite: A d istributed database \, caching and processing platform designed to store a nd compute on large volumes of data across a cluster of nodes - which can be super useful to perform distributed training and inference instantly wi thout massive data transmissions * Apache Mahout : A distributed linear al gebra framework that support multiple distributed backends like Apache Spa rk\, to use by data scientists to quickly implement algorithms and statist ics analysis of data. Practical guide for above Apache projects\, focusing following points: * Data processing\, implementing existing & customised own ML algorithms\, tuning\, scaling up and finally deploying to optimisin g it using Apache cluster management tools and(or) Kubernetes. Performance and benchmark with Kubernetes. * Handling large-scale batch\, streaming d ata & realtime processing. * Caching data or in-memory for faster ML predi ctions\n

\n\n

\nShekhar is passionate about Open Source Softwares and active in various Open Source Projects. During college days he has co ntributed SymPy - Python library for symbolic mathematics \, Data Science related Ruby gems like: daru\, dart-view(Author)\, nyaplot - which is unde r Ruby Science Foundation (SciRuby)\, Bundler: a gem to bundle gems\, NumP y & SciPy for creating the interactive website and documentation website u sing sphinx and Hugo framework\, CloudCV for migrating the Angular JS appl ication to Angular 8\, and few others. He has successfully completed Googl e Summer of Code 2016 & 2017 and mentored students after that on 2018\, 20 19. Shekhar also talked about daru-view gem in RubyConf India 2018 and PyC on India 2017 on SymPy & SymEngine.\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-W1735@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Apache Deep Learning 301 LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTimothy Spann\n\n

\nIn m y talk I will discuss and show examples of using Apache Hadoop\, Apache Ku du\, Apache Flink\, Apache Hive\, Apache MXNet\, Apache OpenNLP\, Apache N iFi and Apache Spark for deep learning applications. This is the follow up to previous talks on Apache Deep Learning 101 and 201 at ApacheCon\, Data works Summit\, Strata and other events. As part of my talk I will walk thr ough using Apache MXNet Pre-Built Models\, integrating new open source Dee p Learning libraries with Python and Java\, as well as running real-time A I streams from edge devices to servers utilizing Apache NiFi and Apache Ni Fi - MiNiFi. This talk is geared towards Data Engineers interested in the basics of architecting Deep Learning pipelines with open source Apache too ls in a Big Data environment. I will walk through source code examples ava ilable in github and run the code live on Apache NiFi and Apache Flink clu sters.\n

\n\n

\nTim Spann is a Principal Field Engineer at Cloude ra in the Data in Motion Team where he works with Apache NiFi\, MiniFi\, K afka\, Kafka Streams\, Edge Flow Manager\, MXNet\, TensorFlow\, Apache Spa rk\, Big Data\, IoT\, Cloud\, Machine Learning\, and Deep Learning. Tim ha s over a decade of experience with the IoT\, big data\, distributed comput ing\, streaming technologies\, and Java programming. Previously\, he was a senior solutions architect at AirisData and a senior field engineer at Pi votal. He blogs for DZone\, where he is the Big Data Zone leader\, and run s a popular meetup in Princeton on big data\, IoT\, deep learning\, stream ing\, NiFi\, blockchain\, and Spark. Tim is a frequent speaker at conferen ces such as IoT Fusion\, Strata\, ApacheCon\, Data Works Summit Berlin\, D ataWorks Summit Sydney\, DataWorks Summit DC\, DataWorks Summit Barcelona and Oracle Code NYC. He holds a BS and MS in computer science.\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-ml-W1815@apachecon.com SEQUENCE:1 DTSTAMP:20200812T151913Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Edge to AI: Analytics from Edge to Cloud with Efficient Movement o f Machine Data LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTimothy Spann\, Paul Vidal\n\n

\nIn this talk\, we will walk you through the simple steps to build and deploy machine learning for sentiment analysis and YOLO object detect ion as part of an IoT application that starts from devices collecting sens or data and camera images with MiNiFi. This data is streamed to Apache NiF i which integrates with Cloudera Data Science Workbench for classification with models in real-time as part of the real-time event stream. We parse\ , filter\, fork\, sort\, query with SQL\, dissect\, enrich\, transform\,ut ilizing TensorFlow and MXNet processors in NiFi\, join and aggregate data as it is ingested. The data is landed in Big Data stores in the cloud for batch and interactive analytics with Apache Flink\, Apache Spark\, Apache Hive\, Apache Kudu and Apache Impala. Utilizing Intel Movidius\, NVidia Je tson Xavier\, NVidia Jetson Nano and Google Coral Edge processors as part of a real-time streaming deep learning flow that includes Deep Learning Cl assification at the edge\, at the gateway\, in the cloud and at every step along the way. Reference: https://blog.cloudera.com/blog/2019/02/integrat ing-machine-learning-models-into-your-big-data-pipelines-in-real-time-with -no-coding/ https://community.cloudera.com/t5/Community-Articles/Edge-to-A I-IoT-Sensors-and-Images-Streaming-Ingest-and/ta-p/249474 https://communit y.cloudera.com/t5/Community-Articles/Using-Cloudera-Data-Science-Workbench -with-Apache-NiFi-and/ta-p/249469 https://github.com/tspannhw/nifi-cdsw\n< /p>\n\n

\nTim Spann is a Principal DataFlow Field Engineer at Cloude ra\, the Big Data Zone leader and blogger at DZone and an experienced data engineer with 15 years of experience. He runs the Future of Data Princeto n meetup as well as other events. He has spoken at Philly Open Source\, Ap acheCon in Montreal\, Strata NYC\, Oracle Code NYC\, IoT Fusion in Philly\ , meetups in Princeton\, NYC\, Philly\, Berlin and Prague\, DataWorks Summ its in San Jose\, Washington DC\, Barcelona\, Berlin and Sydney. https://w ww.youtube.com/watch?v=bOfSnNVum_M&t=397s\n

CATEGORIES:Machine Learning URL;VALUE=URI:https://apachecon.com/acah2020/tracks/ml.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-observability-T0930@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T093000Z DTEND:20200929T101000Z SUMMARY:Improve Apache APISIX observability with Apache Skywalking LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nYuansheng Wang\n\n

\nApa che APISIX is a cloud-native microservices API gateway\, delivering the ul timate performance\, security\, open-source and scalable platform for all your APIs and microservices. Apache SkyWalking: an APM(application perform ance monitor) system\, especially designed for microservices\, cloud-nativ e and container-based (Docker\, Kubernetes\, Mesos) architectures. Through the powerful plug-in mechanism of Apache APISIX\, Apache Skywalking is qu ickly supported\, so that we can see the complete life cycle of requests f rom the edge to the internal service. Monitor and manage each request in a visual way\, and improve the observability of the service.\n

\n\n

\nOpen source enthusiasts\, participated in and contributed to many open source projects\, and wrote some open source e-books. Apache APISIX ppmc. \n

CATEGORIES:Observability URL;VALUE=URI:https://apachecon.com/acah2020/tracks/observability.html#T09 30 END:VEVENT BEGIN:VEVENT UID:acah2020-observability-T1010@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T101000Z DTEND:20200929T105000Z SUMMARY:Another backend storage solution for the APM system LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJuan Pan\n\n

\nThe APM s ystem provides the tracing or metrics for distributed systems or microserv ice architectures. Back to APM themselves\, they always need backend stora ge to store the necessary massive data. What are the features required for backend storage? Simple\, fewer dependencies\, widely used query language \, and the efficiency could be into your consideration. Based on that\, tr aditional SQL databases (like MySQL) or NoSQL databases would be better ch oices. However\, this topic will present another backend storage solution for the APM system viewing from NewSQL. Taking Apache Skywalking for insta nce\, this talking will share how to make use of Apache ShardingSphere\, a distributed database middleware ecosystem to extend the APM system's stor age capability.\n

\n\n

\nAs a senior DBA worked at JD.com\, the r esponsibility is to develop the distributed database and middleware\, and the automated management platform for database clusters. As a PMC of Apach e ShardingSphere\, I am willing to contribute to the OS community and expl ore the area of distributed databases and NewSQL.\n

CATEGORIES:Observability URL;VALUE=URI:https://apachecon.com/acah2020/tracks/observability.html#T10 10 END:VEVENT BEGIN:VEVENT UID:acah2020-observability-T1050@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T105000Z DTEND:20200929T113000Z SUMMARY:Distributed Tracing in Microservices with Apache Karaf and CXF LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrei Shakirin\n\n

\nMi croservice Architectural Pattern suggests splitting of the business domain to several independent bounded contexts exposed as microservices. It brin gs a lot of benefits for teams working on microservices independently\, bu t\, from other side\, complicates the problem analysis and monitoring. Som etimes it is very hard to detect which component causes slowdown and failu re\, to analyse what happens with request spans multiple services. The sol utions for this challenge are distributed tracing and monitoring. Talk wil l introduce and explain basic tracing terminology: span\, trace and contex t. Presenter will show common approaches to distributed tracing using Apac he Karaf\, CXF and Zipkin\, SpringBoot and Sleuth frameworks. Talk contain s some real project examples and demos.\n

\n\n

\nThe areas of his interest are REST API design\, Microservices\, Cloud\, resilient distribu ted systems\, security and agile development. Andrei is PMC and committer of Apache CXF and committer of Syncope projects. He is member of OASIS S-R AMP Work Group and speaker at Java and Apache conferences. Last speaking e xperience: • DOAG 2019\, Nov 2019\, Nurnberg\, Design Production-Ready Sof tware • Karlsruhe Entwickertag 2017\, Mai 2017\, Karlsruhe\, Microservices with OSGi • ApacheCon Europe 2016\, Nov 2016\, Seville\, Microservices wi th Apache Karaf and Apache CXF: Practical Experience • ApacheCon Europe 20 15\, Oct 2015\, Budapest\, Create and Secure Your REST API with Apache CXF • ApacheCon Europe 2014\, Nov 2014\, Budapest\, Design REST Services With CXF JAX-RS Implementation: Lessons Learned • WJAX 2011\, Nov 2011\, Munic h\, Apache Days\, Enabling Services with Apache CXF\n

CATEGORIES:Observability URL;VALUE=URI:https://apachecon.com/acah2020/tracks/observability.html#T10 50 END:VEVENT BEGIN:VEVENT UID:acah2020-observability-T1130@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T113000Z DTEND:20200929T121000Z SUMMARY:The history of distributed tracing storage LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nHongtao Gao\n\n

\nOver t he past few years\, and coupled with the growing adoption of microservices \, distributed tracing has emerged as one of the most commonly used monito ring and troubleshooting methodologies. New tracing tools are increasingly being introduced\, driving adoption even further. One of these tools is A pache SkyWalking\, a popular open-source tracing\, and APM platform. This talk explores the history of the SkyWalking storage module\, shows the evo lution of distributed tracing storage layers\, from the traditional relati onal database to document-based search engine. I hope that this talk contr ibutes to the understanding of history and also that it helps to clarify t he different types of storage that are available to organizations today.\n

\n\n

\nHongtao Gao is the engineer of tetrate.io and the former Huawei Cloud expert. One of PMC members of Apache SkyWalking and participa tes in some popular open-source projects such as Apache ShardingSphere and Elastic-Job. He has an in-depth understanding of distributed databases\, container scheduling\, microservices\, ServicMesh\, and other technologies .\n

CATEGORIES:Observability URL;VALUE=URI:https://apachecon.com/acah2020/tracks/observability.html#T11 30 END:VEVENT BEGIN:VEVENT UID:acah2020-observability-T1210@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T121000Z DTEND:20200929T125000Z SUMMARY:SourceMarker - Continuous Feedback for Developers LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBrandon Fergerson\n\n

\n Today's monitoring solutions are geared towards operational tasks\, displa ying behavior as time-series graphs inside dashboards and other abstractio ns. These abstractions are immensely useful but are largely designed for s oftware operators\, whose responsibilities require them to think in system s\, rather than the underlying source code. This is problematic given that an ongoing trend of software development is the blurring boundaries betwe en building and operating software. This trend makes it increasingly neces sary for programming environments to not just support development-centric activities\, but operation-centric activities as well. Such is the goal of the feedback-driven development approach. By combining IDE and APM techno logy\, software developers can intuitively explore multiple dimensions of their software simultaneously with continuous feedback about their softwar e from inception to production.\n

\n\n

\nBrandon Fergerson is an open-source software developer who does not regard himself as a specialist in the field of programming\, but rather as someone who is a devoted admi rer. He discovered the beauty of programming at a young age and views prog ramming as an art and those who do it well to be artists. He has an affini ty towards getting meta and combining that with admiration of programming\ , has found source code analysis to be exceptionally interesting. Lately\, his primary focus involves researching and building AI-based pair program ming technology.\n

CATEGORIES:Observability URL;VALUE=URI:https://apachecon.com/acah2020/tracks/observability.html#T12 10 END:VEVENT BEGIN:VEVENT UID:acah2020-observability-T1250@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T125000Z DTEND:20200929T133000Z SUMMARY:Why averages lie and how to truely monitor your systems LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nFilipe Costa Oliveira\n\n< p>\nWe spend most of our time looking at the reported averages of our moni toring systems\, completely disregarding the painful truth that the number s that we look at and present to our bosses\, to our business and make dec isions based upon\, do not represent our user experience. This simple fact seems to surprise many people. It feels good looking at steady state moni toring charts. In this session\, you will be told why is it important to p ay to the \"higher end\" of the percentile spectrum in most application mo nitoring\, benchmarking\, and tuning environments and how you can make bet ter usage of the open-source tooling we have at our disposal ( giving exam ples on both OSS HDR and T-Digest Histograms ).\n

\n\n

\nPerforma nce Engineer\, RedisLabs High-performance scientist\, low-level C++ grid a nd distributed computing. Open Source Contributor.\n

CATEGORIES:Observability URL;VALUE=URI:https://apachecon.com/acah2020/tracks/observability.html#T12 50 END:VEVENT BEGIN:VEVENT UID:acah2020-openoffice-W1615@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Apache OpenOffice the Schrödinger App - Quo vadis? LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPeter (petko) Kovacs\n\n

\nThis talk will be a general talk on the situation on the Apache OpenOff ice project. I will outline the situation where we are today. Outline a bi t the difficulties we have moving to the future. To understand the title s ee Clabuurn's article at https://www.theregister.com/2018/10/10/apache_ope n_office_not_dead/ ) Measure position - A short summary on current project state Messure the speed - what is underway and where we go. the project b lur effect - A short view on our difficulties and Gaps that we have\n

\ n\n

\nJoined AOO in Sept 2016 Commiter in 2017\, PMC Memeber in 2017 Chairman at end of 2017 ASF Memember in 2018 ASF Emeritus in end of 2019 Freetime Volunteer\n

CATEGORIES:OpenOffice URL;VALUE=URI:https://apachecon.com/acah2020/tracks/openoffice.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-openoffice-W1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Streiflichter eines langen Weges LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMichael Stehmann\n\n

\nS ome highlights of the long history of OpenOffice\, a nearly 20 years old p roject\, which is an Apache top level project since nearly 8 years. The fo cus is on the german community and the language of the talk will be also g erman.\n

\n\n

\nmember of the germanophone community of OpenOffic e.org\, initial committer of Apache OpenOffice\, member of the PMC of Apac he OpenOffice\, author of an extention for Apache OpenOffice for german la wyers\, former fellow of the FSFE\, \,member of the legal network of FSFE\ , cofounder of Freie Software Freunde e. V.\, recent chair person of this charitable organisation\, Debian user since 2002\n

CATEGORIES:OpenOffice URL;VALUE=URI:https://apachecon.com/acah2020/tracks/openoffice.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-openoffice-W1735@apachecon.com SEQUENCE:49 DTSTAMP:20200828T190237Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Building Apache OpenOffice on MacOS LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJim Jagielski\n\n

\nA ta lk about building Apache OpenOffice on MacOS.\n

\n\n

\nJim Jagiel ski is a well-known and acknowledged expert and visionary in open source\, an accomplished coder\, and frequent engaging presenter on all things ope n\, web\, and cloud related. As a developer\, he’s made substantial code c ontributions to just about every core technology behind the internet and w eb and in 2012 was awarded the O’Reilly Open Source Award. In 2015\, he re ceived the Innovation Luminary Award from the EU. He is likely best known as one of the developers and cofounders of the Apache Software Foundation\ , where he has previously served as both chairman and president and where he’s been on the board of directors since day one. He’s served as presiden t of the Outercurve Foundation and was also a director of the Open Source Initiative (OSI). He works at Uber in their Open Source Program Office. He credits his wife Eileen with keeping him sane.\n

CATEGORIES:OpenOffice URL;VALUE=URI:https://apachecon.com/acah2020/tracks/openoffice.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-openoffice-W1735-2@apachecon.com SEQUENCE:0 DTSTAMP:20200928T220723Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Translation of an application - How does it work LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMechtilde Stehmann\n\n

\ nIn this talk I will show the steps which are need for the translation of Apache OpenOffice. This is an example to do a translation of a very big pr oject\, too. The translation is done at a Pootle Server hosted by ASF. Mos t steps are automated by scripts to handle the translation. Nearly 550000 strings in more than 60 languages\n

\n\n

\nPart of the project si nce 2005 (former OpenOffice.org) Working on translation\, qa and the base modul.\n

CATEGORIES:OpenOffice URL;VALUE=URI:https://apachecon.com/acah2020/tracks/openoffice.html#W1735- 2 END:VEVENT BEGIN:VEVENT UID:acah2020-openoffice-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:OpenOffice UNO Programming with Groovy LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCarl Marcum\n\n

\nThe ta lk will discuss using the Apache Groovy programming language with Apache O penOffice UNO API's and some associated projects that allow this to happen . Projects include the Groovy UNO Extension that adds convenience methods to the OpenOffice API's allowing less coding\, an OpenOffice Extension tha t adds Groovy as a macro language to the office\, and an associated extens ion to add sample macros to the office written in Groovy. Examples of usag es like Groovy scripts as OpenOffice client applications\, OpenOffice macr os in Groovy\, and a compiled OpenOffice extension application in Groovy.\ n

\n\n

\nSoftware developer and Open Source enthusiast. Owner of Code Builders\, LLC specializing in Java-based technologies including Apac he Groovy language and the Grails framework for web applications. Apache O penOffice committer and PMC member. Currently serving as VP OpenOffice. Su n Certified Java Programmer.\n

CATEGORIES:OpenOffice URL;VALUE=URI:https://apachecon.com/acah2020/tracks/openoffice.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-openoffice-R1615@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:OpenOffice on the Web LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDave Fisher\n\n

\nA talk about our Website Infrastructure. - openoffice.org - forum.openoffice.org - wiki.openoffice.org - confluence wikis - bugzilla - mailing lists\n

\n\n

\nDave has been a member of the Apache OpenOffice PMC since Ope nOffice.org started Incubation at the ASF. He ported ported the OpenOffice .org website to the Apache CMS and has helped with Sysadmin tasks with the Forums.\n

CATEGORIES:OpenOffice URL;VALUE=URI:https://apachecon.com/acah2020/tracks/openoffice.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-T0930@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T093000Z DTEND:20200929T101000Z SUMMARY:Transactional event streaming with Apache Pulsar (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nran gao\n\n

\nTransactio nal event streaming with Apache Pulsar The highest message delivery guaran tee that Apache Pulsar provides is `exactly-once`\, producing at a single partition via Idempotent Producer. Users are guaranteed that every message produced to a single partition via an Idempotent Producer will be persist ed exactly once\, without data loss. However\, there is no `atomicity` whe n a producer attempts to produce messages to multiple partitions. From the consumer side\, acknowledgement is a best-effort operation\, which result s in message redelivery\, hence consumer will receive duplicate messages. Pulsar only guarantees `at-least-once` consumption for consumers. It creat es inconvenience and brings in complexity when you use Pulsar to build mis sion critical services (such as billing services). Pulsar introduces trans action support in 2.7.0 version\, to simplify the process of building reli able and fault resilient services using Apache Pulsar and Pulsar Functions . It only provides the capability to achieve end-to-end exactly-once for s treaming jobs in other stream processing engines. This presentation deep d ives into the details of Pulsar transaction and how Pulsar transaction is applied to Pulsar Functions and other processing engines to achieve transa ctional event streaming. How does Pulsar transaction work? How do Pulsar F unctions offer transaction support using Pulsar transaction?\n

\n\n

< em>\nRan Gao is a software engineer at StreamNative. Prior to StreamNative \, he worked at Zhaopin.com and JD Logistics\, responsible for the develop ment of the front-end and back-end of the business system. Being intereste d in open source and messaging systems\, Ran is an Apache Pulsar contribut or.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#T0930 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-T1010@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T101000Z DTEND:20200929T105000Z SUMMARY:Using Apache Pulsar in China Mobile billing system (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSong Xue\n\n

\nTelecommu nication is a complex system. We’ve adopted Apache Kafka\, RocketMQ and ot her messaging systems in our business. However\, they hardly meet our requ irements. When we met Apache Pulsar\, we found it was an ideal solution fo r us. Apache Pulsar has multi-layer and segment-centric architecture\, and supports geo-replication. We can query data with Pulsar SQL\, and create complex processing logic without deploying other systems with Pulsar Funct ions.\n

\n\n

\nSong Xue is a senior software engineer. He is expe rienced in telecommunication\, big data and stream processing.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#T1010 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-T1050@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T105000Z DTEND:20200929T113000Z SUMMARY:Pulsar application in Ksyun cloud log service (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBin Liu\n\n

\nOur log se rvice is a one-stop service for logging data. The services cover log colle ction\, log storage\, log retrieval and analysis\, real-time consumption\, log delivery and so on. Currently\, our service supports log query and mo nitoring for many businesses\, and processes tens of terabytes of data eve ry day. Apache Pulsar is a cloud-native distributed messaging platform wit h multi-layer and segment-centric architecture and multi-tenancy. With Pul sar\, we can scale up partitions and merge partitions easily\, and process millions of topics.\n

\n\n

\nApache open source community contri butor\, tech lead of Ksyun log service\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#T1050 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-T1130@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T113000Z DTEND:20200929T121000Z SUMMARY:The Practice of Apache Pulsar in BIGO (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nHang Chen\n\n

\nPowered by Artificial Intelligence technology\, BIGO's video-based products and se rvices have gained immense popularity\, with users in more than 150 countr ies. These include Bigo Live (live streaming) and Likee (short-form video) . Bigo Live is available in more than 150 countries and Likee has more tha n 100 million users and is popular among the Generation Z. In the past few years\, we have deployed many Kafka clusters to support real-time ETL and short-form video recommendation. The Apache Pulsar's layered architecture and new features\, such as Low latency with durability\, Horizontally sca lable\, Multi-tenancy etc\, help us solve a lot of problems in production. We have adopted Apache Pulsar to build our Message Processing System\, es pecially in Real-Time ETL\, short-form video recommendation and Real-Time Data report. In this talk\, I will share our journal of adopting Apache Pu lsar in our Real-Time Message Processing System\, especially in Flink & Fl ink SQL working with Pulsar. I will also discuss the problems we have enco untered in using Pulsar and experience in performance tuning.\n

\n\n

\nHang Chen is the tech lead of the Messaging Platform team at BIGO. H e is responsible for creating a centralized pub-sub messaging Platform\, w hich provides a vast number of service/application traffics. He introduced Apache Pulsar into their Messaging Platform and integrated it with upstre am and downstream systems\, such as Flink\, ClickHouse and other inner sys tems for Real-Time recommendation and analysis. He focuses on Pulsar perfo rmance tuning\, new features development and Pulsar ecosystem integration. \n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#T1130 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-T1210@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T121000Z DTEND:20200929T125000Z SUMMARY:Work with Apache Pulsar broker interceptors (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPenghui Li\n\n

\nBroker interceptor is a new feature that allows users to add the custom intercept or to intercept Pulsar requests. The broker interceptor enables many enter prise features such as audit log\, reject illegal requests\, and so on. In this talk\, I will show how broker interceptor works\, and how to write a broker interceptor step by step.\n

\n\n

\nPenghui Li is a PMC me mber of Apache Pulsar\, and tech lead in Zhaopin.com\, where he promotes A pache Pulsar proactively. He focuses on messaging service\, including mess aging system\, microservice\, and Apache Pulsar.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#T1210 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-T1250@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200929T125000Z DTEND:20200929T133000Z SUMMARY:Pulsar adoption in SAAS platform (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShaohong Pan\n\n

\nIn th e past\, we used AMQP. The service broke down occasionally and had a serio us negative impact on our business. AMQP does not support multi-tenancy. I came to know Apache Pulsar last year. After investigation\, we found it w as an ideal streaming data platform and could solve our problems quite wel l. In this talk\, I will share how we adopt Pulsar in our parking system. We customize a messaging system with EMQX\, Pulsar and Sink to deal with o ur data in our parking system. The following is a general workflow of our business. Upstream: The real-time data at the parking lot is first transmi tted to EMQX\, and then transmitted to Pulsar with a bridge. The business system processes the data and returns the result to Pulsar. Downstream: Si nk retrieves the result from Pulsar\, and sends it to EMQX\, and then EMQX sends the data to the parking lot. Data analysis: Process data in Hive vi a pulsar-flink connector. Data query: Develop the features of querying dat a in Pulsar Manager via Pulsar SQL and sending data via TOPIC.\n

\n\n

\nShaohong Pan is tech lead of the messaging system(including Pulsar\ , EMQX\, etc.) at Keytop\, a leading smart parking solution provider. He i ntroduced Apache Pulsar to Keytop\, and promote Apache Pulsar in their bus iness proactively.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#T1250 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W0900@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T090000Z DTEND:20200930T094000Z SUMMARY:Application of Apache Pulsar in Tencent Midas Scenario (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDezhi Liu\n\n

\nMidas is an Internet billing platform that supports the 100-billion-level revenue in Tencent's internal business. It integrates domestic and international p ayment channels\, provides various services such as account management\, p recision marketing\, security risk control\, auditing and accounting\, bil ling analysis and so on. The platform carries daily revenue of hundreds of millions of dollars. It provides services for 180+ countries (regions)\, 10\,000+ businesses and more than 1 million settlers. Working as an all-ro und one-stop billing platform\, the total number of its escrow accounts is more than 30 billions. The characteristics of Tencent billing\, a combina tion of financial attributes and massive Internet attributes\, such as ten s of billions of account custody and daily tens of billions of transaction requests\, for such a huge transaction volume and complex business proces ses\,Various asynchronous or abnormal situations require the support of di stributed message queues. The characteristics of pulsar\, the cloud-native storage and computing separation design\, for Tencent's large-scale syste m\, on-demand elastic scaling is very necessary\; millions of topics\, del ayed messages\, any number of consumers\, etc.\, for high concurrency of b illing Such scenes are suitable\; The ability to replicate across regions is also necessary for billing globalization. Combining high consistency\, high reliability and performance considerations\, we currently use pulsar as the core part of the system as the standard method of exception handlin g and communication between services of the consistent transaction engine. It has already carried tens of billions of messages per day and maintains a good stability. In actual operation\, we also found some problems with pulsar. For example\, the dependence on zookeeper is still relatively heav y. At present\, there is no separation of consumption and production of br okers. The cross-region strong consistency is not perfect for node selecti on. We have tried to solve some of the problems. Submit to the community\, and others also communicate and discuss with the community. In general\, pulsar currently meets our needs better. We are also happy to share our ex perience and problems with you\, and look forward to more exchanges with e ach other. Pulsar can be more perfect and have a wider range of applicatio ns under the joint efforts of the community.\n

\n\n

\nFocusing on the development of financial-level distributed components\, he is mainly engaged in the design and development of distributed distributed message t ransactions and transaction engines\, and escorts Tencent's revenue. Pay m ore attention to the field of distributed messaging\, participate in the c onstruction of the Apache Pulsar community\, and introduce the Tencent tra nsaction message bus to the ground.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W0900 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W0940@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T094000Z DTEND:20200930T102000Z SUMMARY:AMQP-on-Pulsar — bring native AMQP protocol support to Apache Puls ar\n(Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nHao Zhang\n\n

\nChina Mo bile is the Gold Member of OpenStack Foundation and has the largest OpenSt ack cluster deployment practice in the world. RabbitMQ is the default inte gration of the message middleware in OpenStack\, and China Mobile has enco untered great challenges in the deployment and maintenance of RabbitMQ. In the OpenStack system\, RabbitMQ\, as an RPC communication component\, has a large number of messages flowing in and out. During the operation proce ss\, there is often a backlog of messages. This will cause memory exceptio ns\, and processes will often be stuck due to memory exceptions. On the ot her hand\, RabbitMQ's mirrored queue is used in order to ensure high avail ability of data. When a node runs into an abnormal state\, the entire clus ter is unavailable regularly. Moreover\, RabbitMQ's programming language e rlang is obscure and difficult to troubleshoot. In summary\, considering t he instability of RabbitMQ cluster\, the difficulty of operation and maint enance\, and the difficulty of troubleshooting\, China Mobile intends to d evelop a middleware product that can replace RabbitMQ. Then China Mobile's middleware team begins to investigate the self-developed technical route of AMQP message queue. By comparing Qpid\, RocketMQ and Pulsar\, China Mob ile is attracted by Pulsar's unique architecture which decouples data serv ing and data storage into separate layers. Apache Pulsar is an event strea ming platform designed from the ground up to be cloud-native- deploying a multi-layer and segment-centric architecture. The architecture separates s erving and storage into different layers\, making the system container-fri endly. The cloud-native architecture provides scalability\, availability\, and resiliency and enables companies to expand their offerings with real- time data-enabled solutions. Pulsar has gained wide adoption since it was open-sourced in 2016 and was designated an Apache Top-Level project in 201 8. So we decided to develop AMQP on Pulsar(AoP). By adding the AoP protoco l handler in your existing Pulsar cluster\, you can migrate your existing RabbitMQ applications and services to Pulsar without modifying the code. T his enables RabbitMQ applications to leverage Pulsar’s powerful features\, such as infinite event stream retention with Apache BookKeeper and tiered storage. I will introduce how we develop AoP\, the architecture\, and how to deploy it in container. Then I will present the performance comparison of AoP and RabbitMQ.\n

\n\n

\nHao Zhang is a senior software eng ineer at China Mobile\, where he specializes in message queue and distribu ted cache with extensive experience in handling high-reliability and high- performance projects. He is also a contributor to Apache Pulsar and Apache RocketMQ.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W0940 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1020@apachecon.com SEQUENCE:2 DTSTAMP:20200828T190237Z DTSTART:20200930T102000Z DTEND:20200930T110000Z SUMMARY:Apache Pulsar in AI data service (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDongliang Jiang\n\n

\nAp pen is a leading company in the AI data service area. When serving a large volume of data collection and annotation\, we faced some challenges on ta sk distribution\, anti-scamming and AI model training. The traditional tas k distribution was based on database\, it has flexibility on messing aroun d data\, but it’s not easy to scale horizontally and has performance issue s when the dataset grows large. We adopt the Apache Pulsar and NoSQL datab ase solution to resolve those pain points and keep the flexibility. We hav e also used Apache Pulsar with Apache Flink in our workload reporting\, an ti-scamming and AI model training for both real-time pipeline and batch pi peline. Apache Pulsar plays a key role in our AI data platform as the data lake to connect all the business features and make each component decoupl ed.\n

\n\n

\nArchitect in Appen China. Have 20 years experience o n high performance computing\, distributed systems and messaging/streaming architectures.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1020 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1100@apachecon.com SEQUENCE:2 DTSTAMP:20200828T190237Z DTSTART:20200930T110000Z DTEND:20200930T114000Z SUMMARY:Unified data processing with Apache Spark and Apache Pulsar (Manda rin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJia Zhai\, Vincent Xie\n\n

\nLambda is widely used in the industry when people need to process bot h real-time and historical data to get a result. It is effective\, and a g ood balance of speed and reliability. But there are still challenges to us e Lambda in the practice. The biggest detraction has been the need to main tain two distinct (and possibly complex) systems to generate both batch an d streaming layers. Thus\, the operational cost of maintaining multiple cl usters is nontrivial\, and in some cases\, one business logic would have t o be split into many segments across different places\, which is a challen ge to maintain as the business grows and it also increases communication o verhead. In this session\, we'd like to present a unique data processing a rchitecture with Apache Spark and Apache Pulsar\, a solution\, with the co re idea of \"One data storage\, one computing engine\, and one API\"\, to solve the problems of Lambda architecture.\n

\n\n

\nJia Zhai is t he co-founder of StreamNative\, as well as PMC member of both Apache Pulsa r and Apache BookKeeper\, and contributes to these two projects continuall y.\n
\nVincent (Weisheng) Xie is the chief data scientist and senior Director at Orange Financial. Previously\, he worked as a tech lead of ML engineering at Intel.\n\n\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1100 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1140@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T114000Z DTEND:20200930T122000Z SUMMARY:Serverless Event Streaming with Pulsar Functions (Mandarin) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nXiaolong Ran\n\n

\nApach e Pulsar is a cloud-native new generation messaging system and real-time p rocessing platform. The messaging system is closely related to the real-ti me computing platform\, and it is often separated and loosely deployed and managed. As the computing component of Pulsar\, the Pulsar function is a fusion and innovation of the message and computing platform in the serverl ess direction. The Pulsar function provides multi-language support for Go\ , Python\, and Java\; and runtimes for threads\, processes\, and Kubernete s. This provides good functionality for users to write\, run\, and deploy functions. Let users only care about the logic of the real calculation\, w ithout complicated configuration or management\; more convenient built-in message-based streaming platform.\n

\n\n

\nXiaolong Ran is a Soft ware Engineer at StreamNative and the committer of Apache Pulsar. The main contributor to Go Functions and pulsar-client-go projects.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1140 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1615@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Pulsar Function Mesh - Complex Streaming Jobs in a Simple Way LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nNeng Lu\, Sijie Guo\n\n

\nPulsar Function is a succinct computing abstraction Apache Pulsar provid es users to express simple ETL and streaming tasks. The simplicity comes i n two folds: Simple Interface and Simple Deployment. As it has been adopte d\, we realized that the native support of organizing multiple functions i nto integrity will be very beneficial. With such support\, people can expr ess and manage multi-stage jobs easily. In addition\, this support also pr ovides the possibility of higher-level abstraction DSL to further simplify the job composition. We call this new feature -- Pulsar Function Mesh. Th is talk aims to provide a thorough walkthrough of this new Pulsar Function Mesh Feature\, including its design\, implementation\, use cases\, and ex amples\, to help people seeking simple streaming solutions understand this newly created powerful tool in Apache Pulsar.\n

\n\n

\nNeng Lu:< br />\nNeng Lu is a staff software engineer at StreamNative where he drive s the development of Apache Pulsar and the integrations with big data ecos ystem. Before that\, he was a senior software engineer at Twitter. He was the core committer to the Heron project and the leading engineer for Heron development at Twitter. He also worked on Twitter’s monitoring and key-va lue storage systems. Before joining Twitter\, he got his master's degree f rom UCLA and a bachelor degree from Zhejiang University.
\nSijie Guo:
\nSijie Guo is the co-founder and CEO of StreamNative. StreamNative is a real-time data infrastructure startup offering a cloud-native event s treaming platform powered by Apache Pulsar for the enterprises. Before Str eamNative\, he co-founded Streamlio. Before Streamlio\, he worked for Twit ter as the tech lead for the messaging infrastructure group\, where he co- created DistributedLog and Twitter EventBus. Before Twitter\, he worked on the push notification infrastructure at Yahoo!. He is also the VP of Apac he BookKeeper and PMC member of Apache Pulsar.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Indestructible storage in the cloud with Apache BookKeeper LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAnup Ghatage\, Ankit Jain\, Charan Reddy Guttapalem\, Karan Mehta\, Venkateswararao Jujjuri\n\n

\n This talk highlights how Apache software\, community\, and corporate inter action works well together. The Salesforce team goes over how they have im plemented a highly durable and available cloud storage service based on Ap ache Bookkeeper. Specifically\, they speak about their requirements\, why they chose Apache BookKeeper and the changes they made in cooperation with the Apache community to make it as cloud-aware. As software is increasing ly deployed in public cloud environments\, foundational platforms such as Apache BookKeeper must also continuously evolve to effectively work in mul ti availability zone environments and be designed to work around problems unique to such environments. We at Salesforce added to BookKeeper the abil ity to function effectively in a Multi-AZ public cloud environment. The fi rst step to this was adding awareness in bookies about their location in t he cluster. Which then enabled zone aware placement policies and handling of entire zone failures. They also go over how all of these functions with out allowing any downtime to upper-level services. All of these changes go hand in hand with the core tenets of Apache BookKeeper's core quorum base d storage principles but rethought to work across availability zones in a cloud-native manner. This talk goes over how we manage various challenges such as the creation of ensembles\, placement\, and replication of ledgers \, tolerance to bookie/zone failures\, upgrade scenarios and backward comp atibility all the while satisfying the durability guarantees promised by B ookKeeper in a public cloud environment.\n

\n\n

\nAnup Ghatage
\nAnup works on Salesforce's Infrastructure platform. Previously\, he h as worked on database internals\, query processing and storage at SAP\, Ci sco Systems and other companies for more than 7 years. Anup holds a BS fro m the University of Pune and an MS from Carnegie Mellon University. Ask hi m to perform some close-up magic / read your mind for you.
\nAnkit Ja in
\nAnkit has worked in Salesforce big data infrastructure for the p ast few years after graduating from Carnegie Mellon University. He is pass ionate about distributed systems and big data.
\nCharan Reddy Guttapa lem
\nCharan is a PMTS\, working on a highly available and durable St orage layer for Database System at Salesforce. He serves as the committer and PMC member for the Apache Bookkeeper project. Previously he worked on Windows Phone client side features and APIs.
\nKaran Mehta
\nKar an has worked in Salesforce big data infrastructure for the past few years after graduating from UC Irvine. Current Apache Phoenix PMC member.
\nVenkateswararao Jujjuri
\nCurrently leading an effort to build a ma ssively scalable\, highly performant distributed storage service at Salesf orce. Previously an Architect and member of the IBM Cloud\, Open Virtualiz ation. Current Apache Bookkeeper PMC member.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:KoP\, AoP and MoP - Facilitating interoperability between differen t messaging protocols in Apache Pulsar LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSijie Guo\n\n

\nPulsar i s a cloud-native event streaming platform that provides the ability to con nect\, store\, and process event streams in real-time. It also provides ma ny different language clients for applications to ingest and consume event s and offers connectors for people to connect Pulsar with external systems easily without writing any code. However\, there are many applications al ready written in other messaging protocols such as JMS\, Kafka\, AMQP\, an d HTTP-based protocols. It is hard for people to rewrite those existing ap plications. In order to reduce the adoption barrier for the existing world \, we at StreamNative introduced the protocol handler mechanism in Pulsar 2.5.0 to allow a Pulsar broker to support different message protocols by r eusing its core event streaming infrastructure (i.e. multi-layered archite cture\, infinite stream storage\, multi-tenancy and etc). It facilitates t he interoperability between different message protocols in Pulsar. In this talk\, we will give a deep-dive into the protocol handler mechanism and s hare the experiences of using this mechanism to support different message protocols (Kafka\, AMQP\, REST\, and etc) and the interoperability in Puls ar.\n

\n\n

\nSijie Guo is the co-founder and CEO of StreamNative\ , which provides a cloud-native event streaming platform powered by Apache Pulsar. Sijie has worked on messaging and streaming data technologies for more than a decade. Prior to StreamNative\, Sijie cofounded Streamlio\, a company focused on real-time solutions. At Twitter\, Sijie was the tech l ead for the messaging infrastructure group\, where he co-created Distribut edLog and Twitter EventBus. Prior to that\, he worked on the push notifica tion infrastructure at Yahoo!\, where he was one of the original developer s of BookKeeper and Pulsar. He is also the VP of Apache BookKeeper and PMC member of Apache Pulsar. You can follow him on twitter.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1855@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Pulsar Functions Deployment Options LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nSanjeev Kulkarni\n\n

\nP ulsar functions bring stream processing capabilities to Pulsar topics with out needing to setup a different cluster for a processing engine. With its simple API and flexible deployment options\, it makes it very easy for ev en novice developers to write stream processing applications that work bot h on their laptop as well as in the data-center. In this talk\, I will go over the different deployment models for Pulsar Functions. We will explore the thread-based\, process-based and Kubernetes based runtime options for running Pulsar Functions. We will also explore different tradeoffs betwee n running functions within the broker vs running them on dedicated functio n workers.\n

\n\n

\nSanjeev Kulkarni works on Splunk's Data Strea m Processor product\, focusing on systems and infrastructure layers. Prior to Splunk\, he was the co-founder of Streamlio that was building next gen eration real time processing engines based on Apache Pulsar. Before that S anjeev was the technical lead for real-time analytics at Twitter where he co-created Twitter Heron. Sanjeev also worked in the Adsense team at Googl e leading several initiatives. He has a MS. in computer science from the U niversity of Wisconsin\, Madison.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-pulsar-W1935@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T193500Z DTEND:20200930T201500Z SUMMARY:Streaming Best Practices with Apache Pulsar for Enabling ML LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDevin Bost\n\n

\nIn this presentation\, we introduce best practices of streaming\, some of the mos t important lessons we can teach about how to build sustainable streaming architectures that transform the enterprise. We will cover innovative arch itectural patterns that combine distributed technologies intended for scal e and show how these best practices open the doors for enabling machine le arning at an unprecedented level. We will discuss best practices for enabl ing ML with Kappa architecture. We will also demonstrate how to leverage a n innovative approach to streaming validation that builds on existing best practices developed at Overstock and accelerates the productionalization of streaming pipelines.\n

\n\n

\nWith over 10 years of experience in the software industry\, Devin has developed software in over 15 differ ent languages. Between his experience of performing data migrations\, appl ying vector calculus for ML\, and building enterprise applications\, he le arned the critical role of data in opening doors of insight into novel mar ket opportunities. He also observed many companies architect their softwar e with the mindset of \"we’ll figure out the data later\,\" only to code t hemselves into life-threatening dead-ends. These observations fueled his i nterests in context-rich stream-based architectures like Kappa that thrive on live data capture and real-time analysis for instant value-creation.\n

CATEGORIES:Pulsar/Bookkeeper URL;VALUE=URI:https://apachecon.com/acah2020/tracks/pulsar.html#W1935 END:VEVENT BEGIN:VEVENT UID:acah2020-royale-T1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Hello\, Royale! LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrew Wetmore\n\n

\nA h igh-level\, task-focused view of Apache Royale\, including a bit of histor y\, how it has evolved\, its core of AS3 and MXML\, what it inherits from Flex and how it differs\, where it is going\, and what you can do with it. \n

\n\n

\nDocumentation and QA specialist during 15 years in the software industry with projects ranging from kitchen-table startups to maj or corporations. Has built several applications using Flex/Royale as the f ront end technology. Chief editor for the Apache Royale project.\n

CATEGORIES:Royale URL;VALUE=URI:https://apachecon.com/acah2020/tracks/royale.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-royale-W1655@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190237Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Apache Royale - moving a Flex app to Royale LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrew Wetmore\, Alina Kazi\n\n

\nThere are many applications of all sizes that are facing their e nd-of-life because they were built in Flex presuming the availability of A dobe Flash. Now that browsers are ending their support of Flash\, we need to either migrate those apps to Royale or throw away a rich code resource. This sessions steps through the process of migrating an existing Flex app lication to Royale. It explores how the two platforms are similar and wher e they diverge\, and the tools that are in place to help you get from the dying app to a one with a new life.\n

\n\n

\nAndrew has 15 years in QA and documentation in the software industry and experience building m any Flex and Royale applications.\n
\nAlina is a participant in the A pache Royale project\, and a former staffer at DBIZ Solutions\, where she was involved in a major migration from Flex to Royale\n

CATEGORIES:Royale URL;VALUE=URI:https://apachecon.com/acah2020/tracks/royale.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-royale-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Apache Royale - starting from a blank file LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrew Wetmore\, Carlos Rovira\n\n

\nThis is how-to-do-it session for people who have not worked w ith Apache Royale\, and do not have experience in the Adobe/Apache Flex ec osystem. Focusing on a model to-do list application using an MVC structure \, the session steps through setting up Royale\, getting the first files c ompiled and running\, and what goes into making a robust\, extensible appl ication that can run on any major browser or as an app on a mobile device. \n

\n\n

\nAndrew Wetmore:
\nAndrew worked in and led documen tation and QA teams in projects ranging from startups to groups in major c orporations for 15 years. He is the main editor for the Apache Royale proj ect.
\nCarlos Rovira:
\nCreator of the Jewel component set for A pache Royale. Author of many tutorials and blog entries on different aspec ts of Royale.\n

CATEGORIES:Royale URL;VALUE=URI:https://apachecon.com/acah2020/tracks/royale.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-royale-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200828T190237Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Apache Royale: Tour de Jewel LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAndrew Wetmore\, Carlos Rovira\n\n

\nA walk-through of the Jewel component set\, using the example s in the Tour de Jewel site\, showing how Royale code achieves a wide rang e of effects and functions for a user interface.\n

\n\n

\nAndrew Wetmore:
\n15 years' experience leading QA and documentation teams fo r software projects large and small. Experience building Flex/Flash and Ro yale applications.
\nCarlos Rovira:
\nCreator of the Tour de Jew el feature and author of many tutorials on using Royale.\n

CATEGORIES:Royale URL;VALUE=URI:https://apachecon.com/acah2020/tracks/royale.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-search-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:Tales From The Trenches: Solr Operations LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMike Drob\n\n

\nThere ar e many pitfalls that a team can fall into when designing and implementing a new Solr-based search application. We will draw on stories from the pres enter's operational experience and distill the events into easy to underst and patterns and anti-patterns. Topics covered would include query pattern s\, indexing patterns\, and shard design.\n

\n\n

\nAn engineer wi th over a decade of distributed systems experience\, Mike has spent most o f his career helping enable others who are using big data platforms. He is a PMC member and committer on several Apache projects\, and strongly beli eves that when people develop breadth in their expertise it builds better software all around. When not working\, he enjoys photography\, dogs\, pho tography of dogs.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-search-T1655@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Improving Search Availability: Striving for more 9s LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nShubhro Roy\n\n

\nAvaila bility is a critical aspect of any distributed system\, especially when yo ur customer's mission critical applications depend on it. But what does av ailability really mean for Search and how do we measure it? Once measured how do we ensure a multi-cluster deployment of Apache Solr with terabyte s cale sharded inverted index hits the holy grail of 4 9s of availability ? How do we automatically detect failures with such systems and what are our options to handle and recover from such failures without human interventi on ? In this talk we will discuss various architectural choices and deploy ment strategies we have adopted at Box to improve availability of search w hile supporting high-throughput\, near real-time indexing\, low latency an d multi-tenancy. We will share our learnings from various issues we have f aced running Solr at scale and how we have address them by building additi onal scaffolding or tweaking Solr itself. Come take a peek under the hood of Box Search.\n

\n\n

\nShubhro enjoys working with data at scale \, be it indexing\, mining or analyzing it. Currently he is part of the Se arch team at Box\, building infrastructure components that enable millions of users to find relevant content. Prior to Box\, Shubhro worked on full text database search at Oracle. He has been working on enterprise search a nd data discovery for the past 8 years after graduating from Carnegie Mell on University with Masters in Information Systems\, specializing in Inform ation Retrieval and Machine Learning.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-search-T1735@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Open Source Docs as Code LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nCassandra Targett\n\n

\n This talk will review the Lucene community's experiences maintaining Solr documentation in the same way we maintain code. Prior to 2016\, the Solr R eference Guide was only in Confluence (cwiki). Despite community agreement that docs are important\, editing them was a separate process that was ea sy to put off. That put a burden on a few committers to update the content for each new release\, and frequently each version's Guide was not comple te for 2-3 months after a release was announced. In 2016 we decided to int egrate the documentation with our source code. We converted Confluence pag es to AsciiDoc files and started generating static HTML pages hosted in ou r main website. These changes allowed committers to update documentation a s they changed the code. In an open source project where everyone is a vol unteer and there are possibly only 1-2 people who understand any feature\, this has been an incredibly empowering change. Today committer maintenanc e of docs is high enough that the Guide requires very little effort to pre pare for publication. All Release Managers can publish it as part of the r elease process\, reducing the burden on the few who knew their way around the old system. This engagement means the Guide can evolve quickly as comm unity needs change. In this talk I'll share how we made these choices\, th e content and build tools we use\, and how other projects can make updatin g docs a natural part of the code change process.\n

\n\n

\nCassan dra has 20 years experience in search and knowledge management. She has be en an Apache Lucene committer since 2013 and a member of the PMC since 201 6. As Director of Engineering at Lucidworks\, she manages the day-to-day w ork of the Solr development team.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-search-T1815@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:An Anatomy of an Answer: Open NLP & Discourse Analysis - based Ind exing LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBoris Galitsky\n\n

\nInd exers usually index all text in documents. However\, once we learn to \"un derstand\" the logic of a plain text\, we will see how bad for a search it is to index the whole thing. Discourse analysis helps to select text frag ment which should be matched with a potential query\, and throw away the r est In this talk we will apply discourse linguistic to practical text sear ch and discover that the majority of indexers which index all text perform very poorly for complex queries. Relying on standard relevance means such as TF*IDF does not alleviate this problem. We will explore how discourse analysis helps search by identifying text fragments which should be indexe d and matched with potential queries\, and those text fragments which woul d mislead the search and make its precision low. We will demonstrate how a discourse analysis - based indexer can be employed relying on Apache Open NLP project. The audience will learn how discourse analysis formalizes a logic of text to be searched and represents it as a discourse tree\, a str ucture to represent a domain-independent logical organization of text esse ntial for finding relevant fragments. We will also discuss how to proceed from search engines like SOLR to chatbots\, where discourse analysis helps with dialogue management.\n

\n\n

\nBoris Galitsky has been prese nting talks on AI over last two decades and at Apache conferences over las t few years. He contributed linguistic and machine learning technologies t o Silicon Valley startups for last 25 years\, as well as eBay and Oracle\, where he is currently an architect of the Digital Assistant project. An a uthor of three computer science books\, 150+ publications and 20+ patents related to search\, he is now working on a book \"AI for CRM\" to be publi shed by Springer in 2021. Boris is Apache committer to OpenNLP where he cr eated OpenNLP.Similarity component which is a basis for search engine and chatbot development.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-search-W1615@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Concurrent Search In Lucene LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAtri Sharma\n\n

\nConcur rent search is not a new feature in Lucene but has been unexplored. This t alk will talk about the basics\, benefits\, when to use and when not to us e and recent improvements in this area. Concurrent search can provide a ma ssive gain for analytical queries\, which are becoming more and more popul ar as data volumes grow. Single threaded queries do not take the complete advantage of available CPU resources -- something that concurrent query fi xes. This talk will take audience through a complete know-hows and integra ting with existing search platforms built using Lucene.\n

\n\n

\n Database and search guy. Apache Lucene and Apache Solr committer. Major co ntributor to PostgreSQL.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-search-W1655@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Solr's new Plugin System LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nDavid Smiley\n\n

\nSolr 8.4 has a new plugin system that portends of a better future much improved from today: (a) load plugins at runtime\, (b) find 3rd party plugins in a registry\, (c) fetch\, install\, and even configure plugins from the comm and line (CLI)\, (d) a more slimmed down Solr distribution that is more se cure. After an overview\, you will see this system demonstrated\, after wh ich you should feel comfortable in trying it out for yourself when you lea ve. Beyond the CLI demonstration\, we'll look behind the covers a bit to s how some of how it works. We'll finish with a discussion of the gaps and t hus what the future hopefully holds as this new mechanism blossoms. You'll learn a bit about what it takes to \"package\" up your plugins too.\n

\n\n

\nDavid Smiley is a prolific Apache Lucene/Solr committer/PMC m ember and ASF member. David has written books\, delivered training\, and s peaks at meetups & conferences on this subject. Ultimately\, his passion h is hacking on Lucene & Solr. He works on search at Salesforce which gracio usly supports these endeavors.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-search-W1735@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Monitoring Apache Solr Ecosystem on Kubernetes LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAmrit Sarkar\n\n

\nKuber netes is fast becoming the operating system for the Cloud and brings a ubi quity that has the potential for massive benefits for technology organizat ions. Applications/Microservices are moved to orchestration tools like Kub ernetes to leverage features like horizontal autoscaling\, fault tolerance \, CICD and more. Apache Solr can be deployed on Kubernetes on a large-sca le for a plethora of use cases. For such scale\, effective metric dashboar ds\, log analytics\, monitoring\, and alerting system is a requirement to make sure abnormal behaviors are detected\, error diagnostics are performe d and the ability to fine-tune the entire ecosystem to reach the best poss ible performance. In this talk\, we discuss and compare various monitoring and analytics tools for the Solr ecosystem running on Kubernetes. From in built features to third-party tools which provide powerful yet easy to use dynamic dashboards and OpenTracing support.\n

\n\n

\nAmrit Sarka r is Cloud Search Reliability Engineer at Lucidworks Inc\, California-base d enterprise search technology company\, with 4+ years experience in searc h domain and big data\, e-commerce and product. He is working primarily on running search-based applications on Kubernetes\, and developing and impr oving core components of Apache Solr.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-search-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Towards an open source tool stack for e-commerce search LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nEric Pugh\, René Kriegler\n\n

\nSearch teams in the e-commerce space want to own their search: the y want to understand how exactly the retrieval works and optimise it accor ding to their specific needs\, both from the user and from the seller pers pective. Implementing search using open source search engines\, such as So lr and Elasticsearch\, seems like a perfect match. Unfortunately\, the ope n source solutions available today aren’t anywhere near reaching parity wi th a commercial solution out of the box\, especially when it comes to opti mizing search relevance and managing individual queries as a merchandiser. This leads to a very difficult buy vs build decision\, especially for sma ller teams that don’t have deep search expertise already and are faced wit h developing significant functionality for digital commerce from scratch. In this session we will introduce Chorus: an initiative to combine open so urce tools and libraries like Querqy (powerful query rewriting library)\, SMUI (a search management UI to boost and bury products and categories)\, and the Quepid\, RRE\, and Quaerite (search relevance assessment and tunin g projects) into a single template to accelerate the development of your o wn e-commerce search\, allowing you to shift from setting up basic search functionality to domain specific optimizations much faster.\n

\n\n

\nEric Pugh:
\nFascinated by the craft of software development\, Er ic Pugh has been involved in the open source world as a developer\, commit ter and user for the past fifteen years. He is a member of the Apache Soft ware Foundation and continues to be very active in the Solr and Tika proje cts\, as well as avidly reads every commit to the Zeppelin project! In bio tech\, financial services\, and defense IT\, he has helped European and Am erican companies develop coherent strategies for embracing open source sof tware. Eric became involved in Solr when he submitted the patch SOLR-284 f or extracting text from binary files (such as PDF and MS Office formats)\, that subsequently became the single most popular patch as measured by vot es! He co-authored the book Apache Solr Enterprise Search Server\, now on its third edition. Today he helps OSC’s clients build their own search tea ms and improve their search maturity\, both by leading projects and by act ing as a trusted advisor.
\nRené Kriegler:
\nRené has been worki ng as a freelance search consultant for clients in Germany and abroad for more than ten years. Although he is interested in all aspects of search an d NLP\, key areas include search relevance and e-commerce search. His tech nological focus is on Solr/Elasticsearch/Lucene. René is founder and co-or ganiser of MICES (Mix-Camp E-Commerce Search - a Berlin Buzzwords partner event). He maintains Querqy - an open source library for query pre-process ing.\n

CATEGORIES:Lucene/Solr/Search URL;VALUE=URI:https://apachecon.com/acah2020/tracks/search.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-T1615@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:No More Silos: Integrating Databases and Apache Kafka LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRobin Moffatt\n\n

\nComp anies new and old are all recognising the importance of a low-latency\, sc alable\, fault-tolerant data backbone\, in the form of the Apache Kafka st reaming platform. With Kafka\, developers can integrate multiple sources a nd systems\, which enables low latency analytics\, event-driven architectu res and the population of multiple downstream systems. In this talk\, we’l l look at one of the most common integration requirements - connecting dat abases to Kafka. We’ll consider the concept that all data is a stream of e vents\, including that residing within a database. We’ll look at why we’d want to stream data from a database\, including driving applications in Ka fka from events upstream. We’ll discuss the different methods for connecti ng databases to Kafka\, and the pros and cons of each. Techniques includin g Change-Data-Capture (CDC) and Kafka Connect will be covered. Attendees o f this talk will learn: * That all data is event streams\; databases are j ust a materialised view of a stream of events. * The best ways to integrat e databases with Kafka. * Anti-patterns of which to be aware. * The power of ksqlDB for transforming streams of data in Kafka.\n

\n\n

\nRob in is a Senior Developer Advocate at Confluent\, the company founded by th e original creators of Apache Kafka\, as well as an Oracle ACE Director (A lumnus). He has been speaking at conferences since 2009 including QCon\, D evoxx\, Strata\, Kafka Summit\, and Øredev. You can find many of his talks online at http://rmoff.net/talks/\, and his blog articles at http://cnfl. io/rmoff and http://rmoff.net/. Outside of work he enjoys drinking good be er and eating fried breakfasts\, although generally not at the same time.\ n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-T1655@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Achieve the event-driven Nirvana with Apache Druid LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAbdelkrim Hadjidj\n\n

\n After two decades of transforming into data-driven organizations\, compani es are moving to the next level: building event-driven organizations. An e vent-driven organization achieves faster insights\, a better customer expe rience and more agility. However\, this transformation requires advanced s kills to make sense of all the events in real-time which put business peop le on the side. In this presentation\, we will review the data architectur es used by the most advanced event driven organizations today. We will dis cuss the challenges they face on delivering the promised business value an d why stream processing technologies like Apache Kafka and Apache Flink ar e not enough to achieve the streaming nirvana. Finally\, we will explain h ow Apache Druid enables self-service BI on event data and allows business users to ask their own questions leading to real-time insights.\n

\n\n< p>\nAbdelkrim is a Data expert with 12 years experience on distributed systems (big data\, IoT\, peer to peer and cloud). He helps customers in EMEA using open source streaming technologies such as Apache Kafka\, NiFi\ , Flink and Druid to pivot into event driven organizations. Abdelkrim is c urrently working as a Senior Solution Engineer at Imply. Previously\, He h eld several positions including Senior Streaming Specialist at Cloudera\, Solution Engineer at Hortonworks\, Big Data Lead at Atos and CTO at Arthea mis. He published several scientific papers at well-known IEEE and ACM jou rnals. You can find him talking at Meetups or worldwide tech conferences s uch as Dataworks Summit\, Strata or Flink Forward. He founded and runs the Future Of Data Meetup in Paris which is a group of 2300+ data and tech en thusiasts.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-T1735@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Incrementally Streaming RDBMS Data to Your DataLake Automagically LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nTimothy Spann\n\n

\nTher e is often data locked in transactional relational systems that you would like to ingest\, transform\, parse\, aggregate\, and store forever in Hado op as wide tables. With the new features in Apache NiFi\, Cloudera Schema Registry\, HBase 2\, Phoenix\, Hive 3\, Kudu\, Spark 2\, Kafka\, Ranger\, Atlas\, Zeppelin and Hue this becomes something you can do at scale withou t the heavy hand processing of yore. Now with the hybrid cloud\, you may w ant to securely ingest to multiple clusters with new tools including Strea ms Replication Manager. They told me it's not ETL or ELT\, exactly it is s o much more. You now have full control over global data assets with full m anagement\, full control and smart dashboards to allow a true enterprise o pen source solution for all your data. With materialized views and the abi lity to federate queries to JDBC and other data sources your fully ACID Hi ve 3 tables allow for you to escape the small scale EDW and be reborn in u nlimited scale data worlds. References: https://community.cloudera.com/t5/ Community-Articles/ETL-With-Lookups-with-Apache-HBase-and-Apache-NiFi/ta-p /248243 https://community.cloudera.com/t5/Community-Articles/Ingesting-RDB MS-Data-As-New-Tables-Arrive-Automagically-into/ta-p/246214 https://commun ity.cloudera.com/t5/Community-Articles/Incrementally-Streaming-RDBMS-Data- to-Your-Hadoop-DataLake/ta-p/247927 https://community.cloudera.com/t5/Comm unity-Articles/Ingesting-Golden-Gate-Records-From-Apache-Kafka-and/ta-p/24 7557 https://www.datainmotion.dev/2020/05/cloudera-flow-management-101-let s-build.html\n

\n\n

\nTim Spann is a Principal DataFlow Field Eng ineer at Cloudera\, the Big Data Zone leader and blogger at DZone and an e xperienced data engineer with 15 years of experience. He runs the Future o f Data Princeton meetup as well as other events. He has spoken at Philly O pen SOurce\, ApacheCon in Montreal\, Strata NYC\, Oracle Code NYC\, IoT Fu sion in Philly\, meetups in Princeton\, NYC\, Philly\, Berlin and Prague\, DataWorks Summits in San Jose\, Berlin and Sydney.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-T1815@apachecon.com SEQUENCE:2 DTSTAMP:20200810T143451Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:Introduction to Event Streams Development with Kafka Streams LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nBill Bejeck\n\n

\nDevelo pers today work with a lot of data. Much of this data is available near re al-time. And it presents the opportunity for businesses and organizations to improve service and deliver more value to users of today's applications . But the question is\, how to manage this incoming stream of records? Vie wing the incoming data as event streams is one way to think about working with data. In recent years\, Apache Kafka has become a defacto standard fo r ingesting record streams. To work with the incoming data\, Apache Kafka provides a Producer and Consumer interface as the basic building blocks fo r sending to and reading records from Kafka. When building a Kafka-based m icroservice\, using the Producer and Consumer clients means handling all t he details of communicating yourself. To enable building event-driven appl ications\, Apache Kafka provides Kafka Streams. Kafka Streams is the nativ e stream procession library for Apache Kafka In this talk\, we'll review K afka and how it can function as a central nervous system for incoming data . From there\, we'll cover how Kafka Producers and Consumers work and how developers can build a microservice using these building blocks. Finally\, we'll transition our application to a Kafka Streams application and demon strate how using Kafka Streams can simplify building a Kafka based microse rvice. Attendees of this presentation will gain the knowledge needed to un derstand how Kafka Streams works and how they can get started using it to simplify the development of applications involving Apache Kafka. Additiona lly\, developers in attendance that aren't familiar with Apache Kafka itse lf will gain an understanding of how it can help their business or organiz ation make effective use of available incoming event streams.\n

\n\n

\nBill Bejeck is working at Confluent as an integration architect on t he Developer Relations Team before that Bill was a software engineer on th e Kafka Streams team for three years. He has been a software engineer for over 17 years and has regularly contributed to Kafka Streams. Before Confl uent\, he worked on various ingest applications as a U.S. Government contr actor using distributed software such as Apache Kafka\, Apache Spark™\, an d Apache™ Hadoop®. Bill is a committer to Apache Kafka and has also writte n a book about Kafka Streams titled Kafka Streams in Action.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200810T143451Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Change Data Capture with Flink SQL and Debezium LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nMarta Paes\n\n

\nChange Data Capture (CDC) has become the standard to capture and propagate commit ted changes from a database to downstream consumers\, for example to keep multiple datastores in sync and avoid common pitfalls such as dual writes (remember? \"Friends don't let friends do dual writes\"). Consuming these changelogs with Apache Flink used to be a pain\, but the latest release (F link 1.11) introduced not only support for CDC\, but support for CDC from the comfort of your SQL couch. In this talk\, we'll demo how to use Flink SQL to easily process database changelog data generated with Debezium.\nAb out the speaker(s):\n

\n\n

\nMarta is a Developer Advocate at Ver verica (formerly data Artisans) and a contributor to Apache Flink. After f inding her mojo in open source\, she is committed to making sense of Data Engineering through the eyes of those using its by-products. Marta holds a Master’s in Biomedical Engineering\, where she developed a particular tas te for multi-dimensional data visualization\, and previously worked as a D ata Warehouse Engineer at Zalando and Accenture.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190238Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Real-Time Stock Processing With Apache NiFi\, Apache Flink and Apa che Kafka LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPierre Villard\, Timothy Spann\n\n

\nWe will ingest a variety of real-time feeds including stocks with NiFi\, filter and process and segment it into Kafka topics. Kafka dat a will be in Apache Avro format with schemas specified in Cloudera Schema Registry. Apache Flink\, Kafka Connect and NiFi will do additional event p rocessing along with machine learning and deep learning. We will store rea l-time feed data in Apache Kudu for real-time analytics and summaries. Apa che OpenNLP\, Apache MXNet\, CoreNLP\, NLTK and SpaCy will be used to anal yse stock trend data in streams as well as stock prices and futures. As pa rt of the stream processing we will also be classifying images and stock d ata with Apache MXNet and DJL. We will also produce cleaned and aggregated data to subscribers via Apache Kafka\, Apache Flink SQL and Apache NiFi. We will push to applications\, message listeners\, web clients\, Slack cha nnels and to email\, To be useful in our enterprise\, we will have full au thorization\, authentication\, auditing\, data encryption and data lineage via Apache Ranger\, Apache Atlas and Apache NiFi. References: https://com munity.cloudera.com/t5/Community-Articles/Real-Time-Stock-Processing-With- Apache-NiFi-and-Apache-Kafka/ta-p/249221\n

\n\n

\nPierre Villard is currently a Senior Product Manager at Cloudera in charge of all the pro ducts around Apache NiFi and its subprojects like the NiFi Registry\, MiNi Fi agents\, etc.. He has been active in the Apache NiFi project for the la st 4.5 years and is a committer and PMC member of the project. Before join ing Cloudera\, Pierre worked at Google and Hortonworks where he helped cus tomers develop solutions on-premises and in the cloud by using many techno logies including Apache NiFi.
\nTim Spann is a Principal DataFlow Fie ld Engineer at Cloudera\, the Big Data Zone leader and blogger at DZone an d an experienced data engineer with 15 years of experience. He runs the Fu ture of Data Princeton meetup as well as other events. He has spoken at Ph illy Open SOurce\, ApacheCon in Montreal\, Strata NYC\, Oracle Code NYC\, IoT Fusion in Philly\, meetups in Princeton\, NYC\, Philly\, Berlin and Pr ague\, DataWorks Summits in San Jose\, Berlin and Sydney.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-W1615@apachecon.com SEQUENCE:2 DTSTAMP:20200812T151913Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Interactive Streaming Data Analytics via Flink on Zeppelin LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nJeff Zhang\n\n

\nFlink i s a powerful distributed streaming engine\, but it requires lots of progra mming skills. Even Flink supports sql\, it is not an easy job for an analy st to use Flink to do streaming data analytics directly. Fortunately\, ano ther apache project Zeppelin integrates Flink and make streaming data anal ytics pretty easy for these data analyst without programming skillset. In this talk\, I would talk about how to use Flink on Zeppelin to do interact ive streaming data analytics. And how to build real time dashboard without writting any html/js code.\n

\n\n

\nJeff has 11 years of experie nce in big data industry. He is an open source veteran\, start to use hado op since 2009 and is PMC of apache project Tez/Livy/Zeppelin and committer of apache Pig. His past experience is not only on big data infrastructure \, but also on how to leverage these big data tools to get insight. He spe aks several times on big data conferences like hadoop summit\, strata + ha doop world. Now he works in Alibaba Group as a staff engineer. Prior that he works in Hortonworks where he develops these popular big data tools.\n< /em>

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-W1655@apachecon.com SEQUENCE:2 DTSTAMP:20200812T151913Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Fast Samza SQL: Stream Processing Made Easy LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nWeiqing Yang\, Aditya Toomula\n\n

\nApache Samza is a distributed stream processing framework that allows users to process and analyze data in real-time. Fast Samza SQL (FS S) is a managed stream processing service\, powering hundreds of Samza pip elines in production across LinkedIn. Use cases like stream repartitioning \, change capture views\, materialized views\, data migration\, and data c aching are the popular ones hosted by FSS. Such stream processing pipeline s are expressed declaratively\, with Samza SQL being the predominant DSL t hat FSS offers. Due to its SQL-like syntax\, rich authoring and testing en vironment\, users can create and deploy their stream processing jobs in a self-serve fashion within a few minutes. FSS also enables creation of stre am processing pipelines programmatically. Users just need to focus on thei r business logic while FSS takes care of the rest\, such as dependency man agement\, resource provisioning\, auto-scaling\, job monitoring\, failure recovery\, etc. In this talk\, we will introduce the overall FSS architect ure\, highlight the unique value propositions that FSS brings to stream pr ocessing at LinkedIn and share the experiences and lessons we have learned .\n

\n\n

\nWeiqing Yang
\nWeiqing has been working in big da ta computation frameworks since 2015 and is an Apache Spark/HBase/Hadoop/S amza contributor. She is currently a software engineer in streaming infras tructure team at LinkedIn\, working on Samza\, Kafka\, etc. Before that\, she worked in Spark team at Hortonworks. Weiqing obtained a Master Degree in Computational Data Science from Carnegie Mellon University. Weiqing enj oys speaking at conferences. She presented in Spark Summit 2017\, HBaseCon 2017\, and KubeCon + CloudNativeCon North America 2019.
\nAditya Too mula
\nAditya has been working at Linkedin in streams infrastructure team since 2016. He has contributed to Apache Samza and Brooklin with late st contributions to Samza Sql and fully managed Samza. He is an Apache Sam za committer and has over 15 years of Software Engineering experience. In his earlier life\, he worked in Storage domain at NetApp\, building variou s kinds of replication products and file systems.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-W1735@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Fresh updates about the new Beam Spark Structured Streaming runner LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nEtienne Chauchot\n\n

\nA pache Beam provides a unified programming model to execute batch and strea ming pipelines on all the popular big data engines. The translation layer from Beam to the chosen big data engine is called a runner. A little more than one year ago\, a new Spark runner based on Spark Structured Streaming framework was started and it has been merged to Beam master since. This t alk will give updates about this new runner showing some added features\, some performance improvements and also things that are yet to come.\n

\ n\n

\nEtienne has been working in software engineering and architect ure for more than 16 years. He is focused on Big Data subjects. He is an O pen Source fan and contributes to Apache projects such as Apache Beam\, Ap ache Flink or Apache Spark. He is a Beam committer and PMC member.\n< /p> CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Pravega: Storage for data streams LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nFlavio Junqueira\n\n

\nT here is no shortage of use cases with elements that continuously generate data: end users posting updates and shopping online\; sensors that periodi cally emit samples\; drones that continuously produce aerial video streams \; connected cars that generate a combination of videos\, images\, and tel emetry\; and server fleets that generate an abundance of telemetry data. O ne common aspect shared by several of these cases is that the sources of d ata are machines\, and at scale\, machines can generate data at extremely high volumes. Machine-generated data creates an important challenge for an alytics systems to ingest\, store and process such high-volumes of machine -generated data in an efficient and effective manner. Pravega is a softwar e system developed from the ground up to enable applications to ingest and store high-volumes of continuously generated data. Pravega exposes the st ream as a core storage primitive\, which enables applications continuously generating data to ingest and store such data permanently. Applications t hat consume stream data from Pravega are able to access the data through t he same API\, independent of whether it is tailing the stream\, reprocessi ng the stream\, or processing historical data. Pravega has some unique fea tures such as the ability of storing an unbounded amount of data per strea m\, while appending transactionally and scaling according to workload vari ations. It uses an underlying segment abstraction not only to implement su ch features\, but advanced ones to support stream applications such as sta te synchronization and key-value tables. In this presentation\, we overvie w Pravega\, including its main features and architecture. We show how to u se Pravega when building streaming data pipelines along with stream proces sors such as Apache Flink. We have implemented Pravega connectors for Flin k that enable end-to-end exactly-once semantics for data pipelines using P ravega checkpoints and transactions. Pravega is an open-source project\, l icensed under the Apache License Version 2.0\, and hosted on GitHub (https ://github.com/pravega/pravega).\n

\n\n

\nFlavio Junqueira is a Se nior Distinguished Engineer at Dell. He holds a PhD in computer science fr om the University of California\, San Diego\, and he is interested in vari ous aspects of distributed systems\, including distributed algorithms\, co ncurrency\, and scalability. His recent work at Dell focuses on stream ana lytics\, and specifically\, on the development of a novel storage system f or streams called Pravega. Before Dell\, Flavio held an engineering positi on with Confluent and research positions with Yahoo! Research and Microsof t Research. Flavio has co-authored a number of scientific publications (ov er 4\,000 citations according to Google Scholar) and an O’Reilly ZooKeeper book on Apache ZooKeeper. Flavio is an Apache Member and has contributed to projects hosted by the ASF\, including Apache ZooKeeper (as PMC and com mitter)\, Apache BookKeeper (as PMC and committer)\, and Apache Kafka.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-W1855@apachecon.com SEQUENCE:1 DTSTAMP:20200812T151913Z DTSTART:20200930T185500Z DTEND:20200930T193500Z SUMMARY:Story of moving our 4Trillion Event Log Pipeline from Batch to Str eaming LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nLohit VijayaRenu\, Zhenzhao Wang\, Praveen Killamsetti\n\n

\nTwitter's LogPipeline handle more tha n 4Trillion events per day. This complex pipeline has evolved over the yea rs to support Twitter's scale of data. This pipeline is designed to be res ilient\, support high throughput and use resources efficiently. Because of its legacy architecture\, it was still batch pipeline at scale. For some time\, our team has been redesigning this to support streaming use cases a nd have done significant architecture changes for this pipeline In this ta lk we deep dive into our old architecture\, highlight pros and cons of tha t and describe how we are making changes for it to be more streaming frien dly. We talk about various open source projects such as Apache Hadoop\, Ap ache Flume\, Apache Tez\, Apache Beam and cloud technologies which tie tog ether to form our large scale event LogPipeline.\n

\n\n

\nLohit V ijayaRenu:
\nLohit is part of DataPlatform team at Twitter. He concen trates on projects around storage\, compute and log pipeline for Twitter s cale both on premise and cloud. He has worked at several startups before j oining Twitter. He has a Masters degree in Computer Science from Stony Bro ok University.
\nZhenzhao Wang:
\nZhenzhao works at Twitter as p art of Hadoop and Log Management team. He is currently concentrating on Tw itter Log Ingestion Pipeline which scales to handle trillions of events pe r day. Previously he was a member of DFS(Pangu) team in Alibaba Cloud wher e he focused on feature for random file access file in Pangu used as stora ge for Virtual Machines. He has Bachelor's degree from Nankai University a nd Master's degree from Tsinghua University.
\nPraveen Killamsetti
\nPraveen works at Twitter as part of the DataPlatform organization. In his current role\, he is working on scaling the log ingestion pipeline to trillions of events in the streaming model and building a data set lifecy cle management system for analytical data sets. He has a master degree in computer science from IIT Madras. Before joining Twitter\, Praveen worked on building distributed storage systems at Nimble Storage\, NetApp and bu ilt various products including Synchronous Replication across multiple dat a centers with automatic failover\, Write Optimized KV stores\, Dedupe and Compression stack\, Efficient Cloning features\, Archiving Storage Snapsh ots to S3 efficiently etc.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#W1855 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-R1615@apachecon.com SEQUENCE:2 DTSTAMP:20200812T151913Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Google Cloud Pub/Sub vs Apache Kafka for streaming solution at sca le LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nPrateek Srivastava\n\n

\ nEvaluation of various technologies to support High speed\, Highly scalabl e REST API to ingest high volume of Analytics payloads from User browsers distributed across the globe. Furthermore\, we will discuss tech stack cho ices\, performance benchmarks\, costing and best practices for implementin g such big data streaming solutions in Google Cloud.\n

\n\n

\nPra teek Srivastava is Technical Architect at Sigmoid. We help organisations r ealize the power of open source to manage big data and leverage AI/ML tech to derive actionable insights.\nHe has more than 13 years of experience i n Big data\, Cloud and Service Oriented architecture and has helped build and sustain several end to end data infrastructures for customers around t he world.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Building your First Connector for Kafka Connect LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRicardo Ferreira\n\n

\nA pache Kafka is rapidly becoming the de-facto standard for distributed stre aming architectures\, and as its adoption grows the need to leverage exist ing data also grows. When developers need to handle certain technologies t hat happen to not have an connector available\; they have no other choice other than write their own. But that can be quite challenging\, even for e xperienced developers. This talk will explain in details what it takes to develop a connector\, how the Kafka Connect framework works\, and what are the common pitfalls that you should avoid. The code of an existing connec tor will be used to explain how the implementation should look like so you can develop more confidence when building your own.\n

\n\n

\nRic ardo is a Developer Advocate at Confluent\, the company founded by the ori ginal co-creators of Apache Kafka. He has over 20 years of experience wher e he specializes in streaming data architectures\, big data\, cloud\, and serverless. Prior to Confluent\, he worked for other vendors\, such as Ora cle\, Red Hat\, and IONA Technologies\, as well as several consulting firm s. When not working\, he enjoys grilling steaks in his backyard with his f amily and friends\, where he gets the chance to talk about anything that i s not IT related. Currently\, he lives in Raleigh\, North Carolina\, with his wife and son. Follow Ricardo on Twitter: @riferrei\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-R1735@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Understanding Data Streaming and Analytics with Apache Kafka LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nRicardo Ferreira\n\n

\nT he use of distributed streaming platforms is becoming increasingly popular among developers\, but have you ever wonder what exactly this is? Part Pu b/Sub messaging system\, partly distributed storage\, partly event process ing engine\, the usage of this type of technology brings a whole new persp ective on how developers capture\, store\, and process events. This talk w ill explain what distributed streaming platforms are and how it can be a g ame changer for modern data architectures. It will be discussed the road i n IT that led to the need of this type of plataform\, the current state of Apache Kafka\, as well as scenarios where this technology can be implemen ted.\n

\n\n

\nRicardo is a Developer Advocate at Confluent\, the company founded by the original co-creators of Apache Kafka. He has over 2 0 years of experience where he specializes in streaming data architectures \, big data\, cloud\, and serverless. Prior to Confluent\, he worked for o ther vendors\, such as Oracle\, Red Hat\, and IONA Technologies\, as well as several consulting firms. When not working\, he enjoys grilling steaks in his backyard with his family and friends\, where he gets the chance to talk about anything that is not IT related. Currently\, he lives in Raleig h\, North Carolina\, with his wife and son. Follow Ricardo on Twitter: @ri ferrei\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#R1735 END:VEVENT BEGIN:VEVENT UID:acah2020-streaming-R1855@apachecon.com SEQUENCE:0 DTSTAMP:20200812T151913Z DTSTART:20201001T185500Z DTEND:20201001T193500Z SUMMARY:Event Streaming and the Data Communication Layer LOCATION:@home X-ALT-DESC;FMTTYPE=text/html:\nAdam Bellemare\n\n

\nStr eaming technologies unlock decoupled\, near real-time services at scale. T he most important part of any streaming platform is the event-broker (eg. Apache Kafka or Pulsar) as it plays the role of the Data Communication Lay er (DCL). Many organizations fail to grasp the importance of the DCL and o ften relegate it to the role of a simple asynchronous message queue\, leav ing their key business domain events locked away in monolithic data stores . This is one of the many pitfalls that will be covered in this presentati on\, along with strategies and tipss for avoiding them. A well constructed DCL decouples both the ownership and production of data from the downstre am services that require access to it. Access to clean\, reliable\, struct ured\, and sorted data streams enables extremely powerful event-driven pat terns. Data becomes much easier to access and no longer relies upon the pr oducer's implementation to serve disparate business requirements. Teams an d products can organize much more clearly along business bounded contexts\ , and modular\, disposable\, and compositional services become extremely e asy to build and test. This presentation covers the best practices\, respo nsibilities of the various actors\, recommendations about specific technol ogical implementations\, and both the organizational changes required and those that will occur as a result of a reliable DCL implementation.\n

\ n\n

\nAdam Bellemare is the author of Building Event-Driven Microser vices (O'Reilly\, 2020). He has been working on event-driven architectures since 2010. His major accomplishments in this time include building an ev ent-driven processing platform at BlackBerry\, driving the migration to ev ent-driven microservices at Flipp\, and most recently\, starting a new rol e to improve event-driven architectures at Shopify. He has contributed to both Apache Avro and Apache Kafka and is a keen supporter of the open-sour ce community.\n

CATEGORIES:Streaming URL;VALUE=URI:https://apachecon.com/acah2020/tracks/streaming.html#R1855 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-T1615@apachecon.com SEQUENCE:88 DTSTAMP:20200828T190238Z DTSTART:20200929T161500Z DTEND:20200929T165500Z SUMMARY:State of the Cat LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Mark Thomas

A review o f the past year or so for Apache Tomcat and a look forward to what is expe cted in the coming 12 months.

I have been an Apache Tomcat c ommitter since November 2003. I initially worked on Tomcat in my free time but since August 2008 I have been employed by SpringSource (now part of V Mware) to work on Apache Tomcat. I spend most of my time working on Tomcat but I also work on tc Server\, VMware's Servlet & JSP container based on Apache Tomcat. I am the release manager Apache Tomcat 8.5\, 9.0 and 10.0 w here I try to release a new version every month or so. I am currently focu sed on Tomcat 10 development which supports Jakarta EE 9. I am a committer for Eclipse Servlet\, Server Pages\, Expression Language and WebSocket. E lsewhere at the ASF\, I am a member of the ASF security and infrastructure teams and I am also on the Commons PMC where I focus on Commons Pool and DBCP. I am a member of the ASF and served as a Director from 2016 to 2019. I have held the position of VP\, Brand Management since February 2018.

\n\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#T1615 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-T1655@apachecon.com SEQUENCE:88 DTSTAMP:20200828T190238Z DTSTART:20200929T165500Z DTEND:20200929T173500Z SUMMARY:Lost in the Docs LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Felix Schumacher

Tomca t has a lot of documentation and a lot of features. We will look at some o f the features that are overlooked or not found but could be handy.

Felix started to use open source at university while trying to com pile Fortran for his math studies. He stayed with Linux but left Fortran f or other languages like Java\, Perl and Python. He continued in working in IT -- building thin clients and management systems for DHCP and DNS. With time his interests faded into looking after a horde of Tomcat servers and felt responsible to make them run faster and more stable. He contributes to both Apache Tomcat and JMeter projects adapting them to his own needs a nd helping others for fun. He gladly became a committer on both projects a nd is a member of the Apache Software Foundation.

\n\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#T1655 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-T1735@apachecon.com SEQUENCE:87 DTSTAMP:20200828T190238Z DTSTART:20200929T173500Z DTEND:20200929T181500Z SUMMARY:Deploying a Production Instance LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Andrew Carr

Deploying Tomcat in a local development environment is a task that most developers a re familiar with. Setting up a consistent\, reliable\, dependable and hard ened Tomcat instance in a production environment is not as difficult as mo st would assume. Here we will discuss important aspects of a Production de ployment\, along with the configuration of other environments\, like Stagi ng\, Integration\, and Quality Assurance. There are pitfalls to avoid\, co mmon tasks to accomplish and automation that can assist in these tasks. < /p>

About: Andrew has been working in the I.T. industry since 1996 developing hardware\, network and software solutions to suit business nee ds and requirements. Leveraging open source software\, he has implemented enterprise software solutions for a number of large corporations while del ivering training to staff\, both entry-level and expert. Currently\, Andre w works as a Consulting Enterprise Architect at Perforce.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#T1735 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-T1815@apachecon.com SEQUENCE:58 DTSTAMP:20200828T190238Z DTSTART:20200929T181500Z DTEND:20200929T185500Z SUMMARY:HTTP/2\, HTTP/3\, and SSL/TLS State of the Art in our Servers (htt pd\, Traffic Server\, and Tomcat) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Jean-Frederic Clere

A new protocol is getting ready HTTP/3 we will look to where we are with it in our serves. The \"old\" HTTP/2 protocol and the corresponding TLS/SSL a re common to Traffic Server\, HTTP Server and Tomcat. The presentation wil l shortly explain the new protocol and the ALPN extensions and look to the state of the those in our 3 servers and show the common parts and the spe cifics of each servers. A demo configuration of each server will be run. < /p>

Jean-Frederic has spent more than 20 years writing client/serv er software. His knowledges range from Cobol to Java\, BS2000 to Linux and /390 to i386 but with preference to the later \;). He is committer in Htt pd and Tomcat and he likes complex projects where different languages and machines are involved. Borne in France\, Jean-Frederic lived in Barcelona (Spain) for 14 years. Since May 2006 he lives in Neuchatel (Switzerland) w here he works for RedHat in the JBoss division on Tomcat\, httpd and cloud /cluster related topics.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#T1815 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-T1855@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190238Z DTSTART:20200929T185500Z DTEND:20200929T193500Z SUMMARY:Split your Tomcat Installation for Easier Upgrades LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Christopher Schultz

Up grading Apache Tomcat can seem like a risky process if your team isn't wel l-versed in the process. Splitting your Tomcat installation into stock ins tall + customized deployment can make the process much less risky and even allow you to quickly downgrade if necessary. We'll explore how to split y our Tomcat installation to get you more comfortable upgrading Tomcat\, red uce deployment times\, and improve your security.

Christopher Schultz is the CTO of Total Child Health\, Inc. where he leads a small te am of engineers to build server-side healthcare-related software in Java. Chris is an ASF Member active in the Apache Tomcat and Velocity communitie s as well as a committer on both projects\, and Tomcat PMC and security te am member. He has attended and spoken at several previous ApacheCon events and helped to organize an Apache BarCamp in the Washington\, DC area.

\n\n\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#T1855 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-T1935@apachecon.com SEQUENCE:1 DTSTAMP:20200828T190238Z DTSTART:20200929T193500Z DTEND:20200929T201500Z SUMMARY:Tomcat: New and Upcoming LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Rémy Mucherat

This ses sion presents the new features that were recently introduced in Apache Tom cat with examples and ideas to take advantage of them\, as well as upcomin g development plans.

Remy is a long time Tomcat committer and ASF member. Lately he's been focusing on various areas such as IO\, ahead of time compilation and optimizations\, and various other additions to To mcat.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#T1935 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-W1615@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20200930T161500Z DTEND:20200930T165500Z SUMMARY:Reverse-Proxying with nginx LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Igal Sapir

\"nginx\, p ronounced \"Engine X\"\, is a high performance Web Server\, Load Balancer\ , and Reverse Proxy\, which has been released as free and open source (Fre eBSD license) since 2004. I will show how to configure nginx to serve as a reverse proxy and load balancer in front of Apache Tomcat backend servers .

Igal has been fascinated with software ever since he got hi s first computer at the age of 12. Based in Los Angeles\, Igal is an Open Source advocate\, and in the past two decades he has been developing web a pplications and helping organizations around the globe to solve issues of scalability\, security\, and performance.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#W1615 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-W1655@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20200930T165500Z DTEND:20200930T173500Z SUMMARY:Tomcat: From a Cluster to a Cloud LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Jean-Frederic Clere

Us ing Tomcat in a cluster and in a cloud. We start by looking how to configu re tomcat to get a cluster and then explore the problems and solutions to have distributed applications running in a cloud. Most cloud providers now have a Kubernetes API. We will look to what we have to add to Tomcat to h ave a decent cloud support for monitoring\, tracing and operating on the c loud. We will show how to use all the pieces. A demo of a cluster will be prepared and run during the presentation and the corresponding application will be moved to a Kubernetes cloud.

Jean-Frederic has spent more than 20 years writing client/server software. His knowledges range f rom Cobol to Java\, BS2000 to Linux and /390 to i386 but with preference t o the later \;). He is committer in Httpd and Tomcat and he likes complex projects where different languages and machines are involved. Borne in Fra nce\, Jean-Frederic lived in Barcelona (Spain) for 14 years. Since May 200 6 he lives in Neuchatel (Switzerland) where he works for RedHat in the JBo ss division on Tomcat\, httpd and cloud/cluster related topics.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#W1655 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-W1735@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20200930T173500Z DTEND:20200930T181500Z SUMMARY:Migrating from AJP to HTTP: It's About Time LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Christopher Schultz

Th e Apache JServ Protocol was developed in 1997 as a proxying protocol betwe en Apache httpd and Apache Jserv. At the time\, mod_proxy was not an optio n for connecting to Apache Jserv\, so Apache mod_jk was developed and gene rations of developers have used it to great effect. But AJP has some serio us flaws\, including lack of encryption and the inability to upgrade conne ctions to use Websockets. In the intervening years\, mod_proxy has become much more fully-featured and can solve all the problems with using AJP. We will cover all of the reasons AJP should be abandoned\, all the nice thin gs mod_jk does for you\, and how to achieve the same results using mod_pro xy with the http and wstunnel child-mods.

Christopher Schultz is the CTO of Total Child Health\, Inc. where he leads a small team of en gineers to build server-side healthcare-related software in Java. Chris is an ASF Member active in the Apache Tomcat and Velocity communities as wel l as a committer on both projects\, and Tomcat PMC and security team membe r. He has attended and spoken at several previous ApacheCon events and hel ped to organize an Apache BarCamp in the Washington\, DC area.

\ n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#W1735 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-W1815@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20200930T181500Z DTEND:20200930T185500Z SUMMARY:Tomcat 10 and Jakarta EE LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Mark Thomas

The move o f Java EE to the Eclipse Foundation and its transformation to Jakarta EE h as resulted in some potentially significant changes for end users. The par t of this session will look at what the changes are\, the impact they have for end users and what the Apache Tomcat project is doing to help mitigat e those impacts. In the second part of the session\, the current progress of Tomcat 10 towards Jakarta EE 9 support will be discussed along with the expected timeline for a stable Tomcat 10.0 release. The final part of the session will look at Jakarta EE 10\, the likely changes and new features and the road map for Jakarta EE 10 support in Apache Tomcat.

I have been an Apache Tomcat committer since November 2003. I initially wo rked on Tomcat in my free time but since August 2008 I have been employed by SpringSource (now part of VMware) to work on Apache Tomcat. I spend mos t of my time working on Tomcat but I also work on tc Server\, VMware's Ser vlet & JSP container based on Apache Tomcat. I am the release manager Apac he Tomcat 8.5\, 9.0 and 10.0 where I try to release a new version every mo nth or so. I am currently focused on Tomcat 10 development which supports Jakarta EE 9. I am a committer for Eclipse Servlet\, Server Pages\, Expres sion Language and WebSocket. Elsewhere at the ASF\, I am a member of the A SF security and infrastructure teams and I am also on the Commons PMC wher e I focus on Commons Pool and DBCP. I am a member of the ASF and served as a Director from 2016 to 2019. I have held the position of VP\, Brand Mana gement since February 2018.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#W1815 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-R1615@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20201001T161500Z DTEND:20201001T165500Z SUMMARY:Getting Started Hacking Tomcat LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Christopher Schultz

So mething bugging you in Tomcat? Think you have a great idea for a feature o r improvement? Documentation needs improvement? Getting started hacking on Tomcat's code or documentation is easy! We'll cover how to get a copy of Tomcat's source code\, build it locally\, communicate with the Tomcat comm itters\, and submit a patch or pull-request.

Christopher Schu ltz is the CTO of Total Child Health\, Inc. where he leads a small team of engineers to build server-side healthcare-related software in Java. Chris is an ASF Member active in the Apache Tomcat and Velocity communities as well as a committer on both projects\, and Tomcat PMC and security team me mber. He has attended and spoken at several previous ApacheCon events and helped to organize an Apache BarCamp in the Washington\, DC area.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#R1615 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-R1655@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20201001T165500Z DTEND:20201001T173500Z SUMMARY:Apache Tomcat and Spring Boot LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Andrew Carr

Discover h ow Spring leverages code provided by the Apache Tomcat project allowing de velopers to quickly prototype and deploy advanced Java web applications. A lot of developers use Spring Boot to bootstrap applications and these app lications frequently end up in production. How does Spring use Tomcat to d eploy your Java application with minimal effort? How much of the Tomcat co de is included in the Spring Boot project? What is the best way to leverag e features offered by both software packages? Dive deep into the workings of Tomcat and Spring\, exploring their interaction.

About: A ndrew has been working in the I.T. industry since 1996 developing hardware \, network and software solutions to suit business needs and requirements. Leveraging open source software\, he has implemented enterprise software solutions for a number of large corporations while delivering training to staff\, both entry-level and expert. Currently\, Andrew works as a Consult ing Enterprise Architect at Perforce.

\n CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#R1655 END:VEVENT BEGIN:VEVENT UID:acah2020-tomcat-R1735@apachecon.com SEQUENCE:0 DTSTAMP:20200914T145203Z DTSTART:20201001T173500Z DTEND:20201001T181500Z SUMMARY:Openly Handling Security Vulnerabilities (Q&A/Panel) LOCATION:@home X-ALT-DESC;FMTTYPE=text/html: Mark Thomas\, Christopher Schultz\, Coty Sutherland

Apache Tomcat is one of the most popular Jav a application servers in the world. The Apache Tomcat Security Team handle s many vulnerability reports via its private \"security\" list each year w here potential and actual vulnerabilities are discussed in a decidedly non -open way to help keep the public safe. At some point\, the software needs to change to address any security shortcomings in the product and all of those changes are available immediately\, and publicly\, to the whole worl d. In this Q&A/panel discussion\, members of the Apache Tomcat Security Te am will discuss how the Apache Tomcat Security Team handles those vulnerab ility reports and manages patches in an open and (mostly) transparent way. Audience participation is highly encouraged\, so come prepared with any q uestions you may have about our processes.

CATEGORIES:Tomcat URL;VALUE=URI:https://apachecon.com/acah2020/tracks/tomcat.html#R1735 END:VEVENT END:VCALENDAR