A practical guide to real-time CDC with Postgres

Change Data Capture (CDC) is a technique employed in event-driven architectures that captures change streams from a source system, like a database, and relays them to various downstream systems, including data lakes and real-time data platforms. In PostgreSQL, CDC utilizes Write-Ahead Logging (WAL) to monitor and capture real-time data changes without impacting the database's performance. This guide illustrates building a real-time CDC pipeline using PostgreSQL as the source, Confluent Cloud for generating and broadcasting events, and Tinybird for consuming these streams and conducting real-time analytics. Hosted on AWS RDS, the PostgreSQL database's changes are captured using the Debezium-based Confluent Postgres CDC Connector, published to a Kafka stream, and ingested by Tinybird, which can create up-to-date API endpoints and manage deduplication at scale. Tinybird’s capabilities in handling CDC event streams make it an effective platform for real-time data analytics, enabling the creation of consolidated views and snapshots of data. The setup involves configuring PostgreSQL for CDC, establishing a Confluent Cloud Kafka cluster, and using Tinybird to connect and process the data, allowing for efficient real-time analytics and data management.