Data Streams Are Nothing Without Actionable Insights Leading to Real-Life Actions

What this post added

This post details the development of a custom stateful connector that bridges Apache Kafka event streams with the Camunda workflow engine for a vehicle maintenance use case. It addresses challenges in translating raw sensor data into actionable insights by implementing solutions for duplicate event prevention, time windowing (using ksqlDB or Kafka Streams), and semantic aggregation. The custom connector, built with Java and Kafka/Camunda clients, maintains a persistent history to enable semantic aggregation and stream replay, facilitating the triggering of workflow instances (e.g., scheduling maintenance) or cancellation based on event streams.