Exploring Change Data Capture with Debezium and Jupyter

When people think about Debezium integrations, the first idea is usually a production pipeline: capture changes from a database, deliver them to Kafka, and route them to downstream services or analytical systems. That is, of course, a very common and very good use case. But there is another side to change data capture that deserves attention too: exploration. Sometimes you do not want to start with a full deployment and a chain of consumers.