Stephan Ewen is CTO and co-founder at Ververica where he leads the development of the stream processing platform based on open source Apache Flink. He is also a PMC member and one of the original creators of Apache Flink. Before working on Apache Flink, Stephan worked on in-memory databases, query optimization, and distributed systems. He holds a Ph.D. from the Berlin University of Technology.

Abstract talk
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.