Implementing Change Data Capture (CDC) in Azure Data Factory: A Step-by-Step Guide

Introduction: Why Use Change Data Capture (CDC) in Azure Data Factory?

In today’s data-driven world, real-time and incremental data processing has become a necessity for businesses striving to stay competitive. Change Data Capture (CDC) is a technique that tracks and identifies changes — such as inserts, updates, and deletes — in a data source and efficiently replicates them to target systems. This eliminates the need to process entire datasets repeatedly, saving time, resources, and computational costs.

Azure Data Factory (ADF), a powerful data integration service, provides seamless support for implementing CDC, enabling users to handle data changes in a highly scalable and efficient way. CDC in ADF is particularly beneficial for scenarios such as real-time analytics, data synchronization, and event-driven architectures. Its ability to integrate with various data sources and its built-in support for incremental loading make it an ideal choice for businesses looking to streamline their ETL/ELT pipelines.

By using CDC in Azure Data Factory, users can minimize data latency, reduce costs, and ensure that their downstream systems always operate with the latest data, making it a go-to solution for modern data integration challenges.

Change Data Capture: the name itself tells that it is used to capture the change in the data. It is a new native top-level…