Stream processing is becoming something like a ""grand unifying paradigm"" for data processing. Outgrowing its original space of real-time data processing, stream processing is becoming a technology that offers new approaches to data processing (including batch processing), real-time applications, and even distributed transactions.
We will take a look at these developments from the view of Apache Flink and present some of the major efforts in the Flink community to build a unified stream processor data processing and data-driven applications. Flink already powers many of the world's most demanding stream processing applications. We present the approach of Flink's next generation streaming runtime that also offers a state-of-the-art batch processing experience and performance. A new Machine Learning library, built on top of a unique new API supports many algorithms to train dynamically across static and real-time data. Finally, we look at new building blocks stream processing offers for data-driven applications that open a new direction to solve application consistency.
With use cases from different users, we show how companies apply this broader streaming paradigm in practice.
Timo Walther is a committer and PMC member of the Apache Flink project. He studied Computer Science at TU Berlin. Alongside his studies, he participated in the Database Systems and Information Management Group there and worked at IBM Germany. Timo joined the project before it became part of the Apache Software Foundation. Today he works as a senior software engineer at Ververica. In Flink, he is mainly working on the Table & SQL ecosystem.