Flink SQL in action
Processing streaming data with SQL is becoming increasingly popular. Fabian Hueske explains why SQL queries on streams should have the same semantics as SQL queries on static data. He then shares a selection of common use cases and demonstrates how easily they can be addressed with Flink SQL.
| Talk Title | Flink SQL in action |
| Speakers | Fabian Hueske (Ververica) |
| Conference | Strata Data Conference |
| Conf Tag | Big Data Expo |
| Location | San Francisco, California |
| Date | March 26-28, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Stream processing is being rapidly adopted by the enterprise. While in the past, stream processing frameworks mostly provided Java- or Scala-based APIs, stream processing with SQL is growing increasingly popular because it makes stream processing accessible to nonprogrammers and significantly reduces the effort to solve common tasks. About three years ago, the Apache Flink community started adding SQL support to process static and streaming data in a unified fashion. Today, Flink SQL powers production systems at Alibaba, Huawei, Lyft, and Uber. Fabian Hueske discusses the current state of Flink’s SQL support and explains the importance of Flink’s unified approach to process static and streaming data. After covering the basics, he shares common real-world use cases ranging from low-latency ETL to pattern detection and demonstrates how easily they can be addressed with Flink SQL.
