Implementing Change Data Capture (CDC) with Estuary Flow for Efficient Data Replication

Introduction

In modern data-driven organizations, real-time data replication is crucial for ensuring data consistency across distributed systems, enabling real-time analytics, and powering operational systems. Traditional approaches to data replication, such as batch processing, often introduce latency, strain source databases, and are inefficient for applications requiring up-to-the-minute insights.

This is where Change Data Capture (CDC) comes into play. CDC allows organizations to capture and replicate only the data changes (inserts, updates, and deletes) in real time, without the overhead of querying entire datasets. When paired with a platform like Estuary Flow, CDC can be seamlessly integrated into a data pipeline, replicating changes across systems without burdening source databases.

In this tutorial, we’ll guide you through the process of implementing CDC with Estuary Flow, focusing on a practical use case with PostgreSQL. By the end of this guide, you’ll have a working CDC pipeline that replicates data changes from a PostgreSQL database to a target system in real time.

Understanding Change Data Capture (CDC)

Change Data Capture (CDC) is a method for tracking changes — such as inserts, updates, and deletes — in a source database. Instead of reprocessing entire datasets during replication, CDC monitors the database logs for changes and propagates only…