Stream Processing is a concept used to act on real-time streaming data. This session shows and demos how teams in different industries leverage the innovative Streams API from Apache Kafka to build and deploy mission-critical streaming real time application and microservices.
The session discusses important Streaming concepts like local and distributed state management, exactly once semantics, embedding streaming into any application, deployment to any infrastructure. Afterwards, the session explains key advantages of Kafka's Streams API like distributed processing and fault-tolerance with fast failover, no-downtime rolling deployments and the ability to reprocess events so you can recalculate output when your code changes.
The session also introduces KSQL - the Streaming SQL Engine for Apache Kafka. Write SQL streaming queries with the scalability, throughput and fail-over of Kafka Streams under the hood.
The end of the session demos how to combine any custom code with your streams application (either Kafka Streams or KSQL) by an example using an analytic model built with any machine learning framework like Apache Spark ML or TensorFlow.