The need to enrich a fast, high volume data stream with slow-changing reference data is probably one of the most wide-spread requirements in stream processing applications. Apache Flink's built-in join functionalities and its flexible lower-level APIs support stream enrichment in various ways depending on the specific requirements of the use case at hand. In this webinar, I like to provide an overview of the basic methods to enrich a data stream with Apache Flink and highlight use cases, limitations, advantages and disadvantages of each.
As Head of Product at Ververica, Konstantin supports multiple product teams working on Apache Flink, Stateful Functions and Ververica Platform in both feature discovery as well as delivery. He is currently focusing on Stateful Functions, the youngest project of the Apache Flink family. Previously, he has been leading the solutions architecture team, helping our clients as well as the Open Source community to get the most out of Apache Flink.