Change data capture is a software process that identifies and tracks changes to the data within a database. It keeps systems in sync, provides reliable data replication and zero downtime cloud migration.
Some methods of CDC are listed below:
-Timestamps on rows: the timestamp is compared to last capture time to identify the change in data.
-Version number in rows: each time a row is updated, a version number is incremented.
-Status indicators on rows: marking the rows which have been updated with an indicator e.g. true or false
-Trigger based CDC: when certain events occur
-Log based CDC: logs every transaction that alters the database, and CDC tools read from these logs to detect the change.
-Diff based CDC: taking a snapshot and comparing to a previous snapshot.
The benefits of CDC include:
-Highly efficient.
-Supports real time analytics.
-Well supported for moving data into a stream processing solution.
Ensures multiple systems stay in sync.
Use Cases of CDC:
-Cloud Migration
-Operational Analytics: retail chains can immediate analysis of sales trends and inventory levels
-Fraud detection: financial institution can use the CDC to track account activities and flag suspicious transactions
-Real time marketing campaigns: capture customer interactions and purchase behaviors to instantly send personalised ads
-Audit: a healthcare provider can maintain a historical record of patient data changes; this guarantees that they can comply with regulatory requirements and provide an accurate audit trail