How To Use Streaming Joins with Apache Flink®

Streaming data brings with it some changes in how to perform joins. In this video, David Anderson and Dan Weston talk about how and when to use temporal joins to combine your data. To learn more, check out our documentation on Apache Flink® SQL joins: https://cnfl.io/47OOaz0 ADDITIONAL RESOURCES ► Flink 101—Event Time and Watermarks: https://cnfl.io/3uEAB75 ► Exactly-Once Processing in Apache Flink:    • Exactly-Once Processing in Apache Flink   ► How to Analyze Data from a REST API with Flink SQL:    • How to Analyze Data from a REST API with F...   CHAPTERS 0:00 - Intro to streaming joins 1:34 - Stateless, materializing, and temporal operations 3:02 - Streaming joins are continuous queries 4:30 - Demo: join with an updating table 8:14 - Demo: join with an appending table 10:31 - Demo: temporal join with a versioned table 13:08 - Conclusion -- ABOUT CONFLUENT Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io. #apacheflink #flink #confluent