Learn how AthenaX, Uber's streaming analytics platform enables users to run production-quality, large scale streaming analytics using SQL. This discussion highlights the design and architecture of AthenaX, and also Uber's production experience.
Learn how Alibaba has explored Flink's potential as an execution engine for streaming and batch processing.
Fabian Hueske and Shuyi Chen share their experience using Flink SQL in production at Uber, explaining how Uber leverages Flink SQL to solve its unique business challenges and how the unified stream and batch processing platform enables both technical or nontechnical users to process real-time and batch data reliably using the same SQL at Uber scale.
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