A practical guide to real-time CDC with MongoDB

Change Data Capture (CDC) is a design pattern that enables the tracking and real-time or near real-time propagation of data changes, such as inserts, updates, and deletes, from a source database like MongoDB to downstream systems without directly querying the source database. This blog post details the implementation of a CDC pipeline using MongoDB Atlas, Confluent Cloud, and Tinybird, emphasizing how Confluent Cloud captures MongoDB change streams using its Kafka Connector and Tinybird analyzes these changes for real-time analytics. Tinybird acts as an effective data sink by processing MongoDB's oplog for real-time analytics, transforming, aggregating, and exposing data changes via high-concurrency, low-latency APIs. The post contrasts this approach with Debezium, a popular open-source framework for CDC, and highlights the benefits of using Tinybird for operational intelligence and event-driven architecture support. The guide also covers deduplication strategies essential for large-scale CDC and provides a hands-on setup for connecting MongoDB Atlas with Confluent Cloud and Tinybird to create scalable real-time analytics systems.