Debezium is an open source distributed platform for change data capture. Start it up, point it at your databases, and your apps can start responding to all of the inserts, updates, and deletes that other apps commit to your databases. Debezium is durable and fast, so your apps can respond quickly and never miss an event, even when things go wrong.

Debezium's platform is  dedicated to providing insights and resources for developers and data engineers interested in change data capture (CDC) and event streaming architectures. Through articles, tutorials, and documentation, Debezium offers insights into capturing database changes, streaming data to Apache Kafka, and building real-time data pipelines. Developers can learn about CDC connectors, data integration patterns, and event-driven architectures to enable real-time analytics and stream processing.

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A walkthrough of using Debezium Engine inside a Jupyter notebook via the pydbzengine Python library to capture PostgreSQL change events interactively. The setup uses two Docker containers (PostgreSQL and JupyterLab), configures a real Debezium PostgreSQL connector, collects CDC events into a pandas DataFrame, and enables ad-hoc analysis of snapshots, inserts, updates, and deletes. The post argues that interactive notebooks are valuable for exploring CDC event structure, validating connector configs, and teaching CDC concepts before committing to a full production pipeline.

Exploring Change Data Capture with Debezium and Jupyter