Streaming Analytics (or Fast Data) is becoming an increasingly popular subject in enterprise organizations. The reason for this is that customers want to have real-time experiences, such as notifications and advise based on their online behavior and other users’ actions.

In this talk, Bas will present a streaming analytics engine that is powered by Apache Flink and written in Scala. Kafka is used for the message bus and Cassandra for the state management. The machine learning models are made with Knime and Spark, exported to PMML format, and evaluated using the Openscoring.io library. Bas will explain the architecture of the framework, demonstrate how to do the setup and integration of Flink jobs and show code examples of typical streaming concepts such as event time, windows, watermarks, and exactly-once processing.

After this session, the attendees will have a good overview of a typical streaming analytics solution and have a better understanding of Apache Flink as a key data streaming technology. Moreover, concepts that might seem vague and complex have been explained with code examples to lower the threshold for creating fast data applications.