► GET STARTED WITH FLINK SQL: https://cnfl.io/apache-flink-sql-module-1

Apache Flink® SQL makes it easy to implement analytics that summarize important attributes of real-time data streams.

There are four different types of time-based windows in Flink SQL: tumbling, hopping, cumulating, and session. Learn how these various window types behave, and how to work with the table-valued functions that are at the heart of Flink SQL’s support for windowing.

For more information, check out the Apache Flink SQL course on Confluent Developer: https://cnfl.io/apache-flink-sql-module-1

RELATED RESOURCES
► OVER aggregations - https://cnfl.io/4fz5Um7
► Exercise: Streaming analytics - https://cnfl.io/4eUkuVf

CHAPTERS

00:00 - Intro

00:41 - Tumbling windows

01:59 - Window table-valued functions

02:40 - What is window_time?

03:02 - Avoid this pitfall!

03:49 - Hopping windows

04:20 - Cumulating windows

05:02 - Session windows

05:36 - Conclusion

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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.

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