Change Data Capture (CDC) is a technique used to identify and capture changes made to data in a database so that those changes can be migrated or synchronized with another system. The primary role of CDC in data movement is to ensure that any updates, deletions, or insertions in the source database are accurately reflected in the target system, whether that’s another database, a data warehouse, or a data lake. By tracking these changes in real-time or near-real-time, CDC helps to minimize the data latency between the source and target systems and ensures that data remains consistent across different environments.
One of the main benefits of CDC is that it allows for efficient data movement without the need for full data extracts and transfers each time there’s an update. For example, consider an online retail application storing transaction data in a relational database. When a customer makes a purchase, the system updates several tables to reflect the new order. Using CDC, only the changes related to that purchase—the new record in the orders table and any updates to inventory—would be captured and sent to a business analytics platform. This means less data is transferred over the network, reducing the load on both the source and target systems, and improving performance.
In addition, CDC supports various use cases, such as data replication, real-time analytics, and data integration processes. For instance, in a scenario where an organization maintains a separate analytics database for reporting, CDC can be employed to keep this database in sync with the operational database reliably. By capturing the specific changes that occur rather than refreshing the entire dataset at regular intervals, organizations can gain timely insights from their data while also maintaining operational efficiency. Overall, CDC plays a critical role in achieving effective and responsive data movement in modern data architectures.