What change data capture is
Change data capture, often shortened to CDC, is a set of techniques for identifying and tracking the changes made to the data in a database. Instead of looking at the whole dataset, CDC focuses on what has changed - the inserts, updates, and deletes. Capturing these changes allows them to be acted on or sent to other systems, keeping different places in sync with the latest data.
The problem it solves
Often, when data changes in one database, other systems need to know about those changes - for example, to keep a copy up to date or to trigger some action. Repeatedly scanning the entire dataset to find what changed would be slow and wasteful. Change data capture solves this by tracking just the changes as they happen, so only the new information needs to be processed.
How CDC works
Change data capture works by detecting changes to the data and recording them so they can be used. There are different ways to do this, such as watching the database's record of changes. The captured changes can then be delivered to other systems. The key idea is that the system identifies what has changed and makes that information available, rather than requiring a full comparison of the data.
Keeping systems in sync
A major use of change data capture is keeping multiple systems synchronized. When data changes in a source database, CDC can propagate those changes to other databases, caches, or analytics systems, so they reflect the latest state. By moving only the changes rather than copying everything repeatedly, CDC makes keeping systems in sync efficient and timely.
Where CDC is used
Change data capture is used in data integration, replication, and analytics, wherever changes in one system need to flow to others. It is common in pipelines that feed data warehouses or other systems, allowing them to stay current without constant full reloads. By focusing on changes, CDC supports efficient, up-to-date data flows across an organization's systems.
Why it matters
Change data capture is a key technique for moving data changes efficiently between systems. Understanding what CDC is - identifying and tracking changes in a database so they can be propagated - clarifies how modern data systems stay in sync without constantly copying everything. For anyone interested in data engineering, it is a valuable concept.
Related on Skillo
See also: What is a data pipeline? Explained, What is a database index? Explained.
Sources
Published date reflects the original event date (2024-04-30). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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