Dynamically Configured Stream Processing using Flink & Kafka

We have built a Flink-based system to allow our business users to configure processing rules on a Kafka stream dynamically. Additionally it allows the state to be built dynamically using replay of targeted messages from a long term storage system. This allows for new rules to deliver results based on prior data or to re-run existing rules that had breaking changes or a defect. Why we submitted this talk: We developed a unique solution that allows us to handle on the fly changes of business rules for stateful stream processing. This challenge required us to solve several problems -- data coming in from separate topics synchronized on a tracer-bullet, rebuilding state from events that are no longer on Kafka, and processing rule changes without interrupting the stream.