Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that are proven to boost application performance and solve scalability problems by storing and processing unlimited data sets distributed across a cluster of interconnected machines. The session is tailored for 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 data grids combined with a technical deep-dive based on Apache Ignite in-memory computing platform. In particular, we'll cover the following essentials of distributed in-memory systems: * Data partitioning: utilizing all memory and CPU resources of the cluster * Affinity co-location: avoiding data shuffling over the network and using highly-performant distributed SQL queries * Co-located processing: eliminating network impact on the performance of our applications

Denis Magda is an open-source enthusiast who started his journey in Sun Microsystems as a developer advocate 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 come across Denis at conferences, workshops and other events sharing his knowledge about the open-source, community building, distributed systems.

Apache Ignite is a powerful in-memory computing platform. The Apache IgniteSink streaming connector enables users to inject Flink data into the Ignite cache. Join Saikat Maitra to learn how to build a simple data streaming application using Apache Flink and Apache Ignite. This stream processing topology will allow data streaming in a distributed, scalable, and fault-tolerant manner, which can process data sets consisting of virtually unlimited streams of events. Apache IgniteSink offers a streaming connector to inject Flink data into the Ignite cache. The sink emits its input data to the Ignite cache. The key feature to note is the performance and scale both Apache Flink and Apache Ignite offer. Apache Flink can process unbounded and bounded data sets and has been designed to run stateful streaming applications at scale. Application computation is distributed and concurrently executed in clusters. Apache Flink is also optimized for local state access for tasks and does checkpointing of local state for durability. Apache Ignite provides streaming capabilities that allow data ingestion at a high scale in its in-memory data grid.

Saikat Maitra is Lead Engineer at Target and Apache Ignite Committer and PMC Member. Prior to Target, 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.