This talk is about some Flink use cases and basic requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.
There is also a highlight of how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning especially online ML, and serverless computation, and how Flink fits with its distinct value.
Learn how Alibaba has explored Flink's potential as an execution engine for streaming and batch processing.
Learn how Alibaba shares their experiences on developing algorithms on Apache Flink and build web UI and client to help people easily use algorithms on data analysis, training and inferencing with machine learning model.
Search and recommendation system for Alibaba’s e-commerce platform use batch and streaming processing heavily. Flink SQL and Table API (which is a SQL-like DSL) provide simple, flexible, and powerful language to express the data processing logic. More importantly, it opens the door to unify the semantics of batch and streaming jobs. In this talk, Shaoxuan Wang and Xiaowei Jiang both from Alibaba share their experience of running large scale Flink SQL and TableAPI jobs in Alibaba Search.
arge-scale streaming processing with Flink and Flink SQL at Alibaba
Source: Seattle Apache Flink Meetup
Also see: Hadoop Weekly 247