The key points of the text revolve around how event streaming can be used to build active workflows in real-time, enabling business actions based on sensor data from vehicles, for instance. A concrete example is presented where a vehicle fleet sends constant sensor data, which needs to be transformed into actionable insights by a stateful connector that starts a new process instance only once for every new insight, while also informing the existing one if the problem no longer exists. This application involves challenges such as idempotency, scalability, and semantic aggregation of multiple insights, which were addressed using techniques like de-duplication, time windowing, and persistence. The solution was implemented using Apache Kafka and Camunda workflow engine, with a custom connector providing the necessary functionality to connect the streams to the workflow engine while keeping track of actions for semantic aggregation.