Streaming


Track Chairs : Yu Li

Streaming data processing is a big deal in big data these days, businesses crave ever-more timely insights into their data, and what was once a ‘batch’ mindset is quickly being replaced with stream processing. More and more companies, small and large, are rethinking their architecture with real-time context at the forefront, and starting to build their streaming platforms with powerful open source engines such as Apache Flink, Apache Spark, Apache Kafka, Apache Pulsar, Apache Storm, etc.

In this topic, you will not only learn about the practical experience of first-line users in applying these Apache projects to their in-production environment, but also the latest developments in the ecology of these Apache projects, and visions on where streaming technology is heading in the future.

2021-08-07

13:30 GMT+8 Flink's Greatest and Latest at Alibaba Chinese Session Yuan Mei

14:10 GMT+8 Practice of Batch-Streaming Unification in Netease Music Chinese Session Lei Wang

14:50 GMT+8 The Application of Spark Structured Streaming in the near real-time data lake scenarios Chinese Session ZhiweiPeng

15:30 GMT+8 Meituan's practice of building the real-time data warehouse platform Chinese Session Chuxi Tang

16:10 GMT+8 Evolution and typical scenes of Flink-based real time computing platform in Qihoo 360 Chinese Session Fan Xinpu

2021-08-08

13:30 GMT+8 Advanced real-time and batch Analytics using Apache Druid English Session Tijo Thomas

14:10 GMT+8 Structured Data Streaming English Session Shivji Kumar Jha

14:50 GMT+8 FLaNK Stack with Flink For Streaming Use Cases English Session Timothy Spann

15:30 GMT+8 REAL-TIME MACHINE LEARNING WITH PULSAR FUNCTIONS English Session David Kjerrumgaard

16:10 GMT+8 Interactive Realtime Dashboards on Data Streams using Apache Kafka, Druid and Superset English Session Nishant Bangarwa

16:50 GMT+8 Apache Druid real-time ingestion challenges and best practices English Session Tijo Thomas