Change Data Capture (CDC) and Slowly Changing Dimension (SCD) are both concepts related to data management, but they serve different purposes. CDC is a data replication approach, while SCD is a dimension management technique used in data warehousing.
Here's a breakdown of their differences:
| Feature | Change Data Capture (CDC) | Slowly Changing Dimension (SCD) |
|---|---|---|
| Purpose | Identifies and processes changed data quickly. Makes changed data available for further use. | Stores and manages current and historical data over time within a data warehouse dimension. |
| Focus | Capturing changes in source systems. | Managing dimension data that changes over time. |
| Implementation | Involves tracking changes at the source database level (e.g., using transaction logs or triggers). | Involves designing dimension tables to accommodate changes, using techniques like adding new rows or updating existing rows with history. |
| Use Case Example | Replicating data from an operational database to a data warehouse or data lake in near real-time. | Tracking customer address changes or product price fluctuations over time in a data warehouse. |
In essence:
- CDC focuses on capturing and propagating data changes from source systems.
- SCD focuses on how to store and manage dimension data changes within a data warehouse.
Think of it this way: CDC is like a surveillance camera that records all the changes happening (data updates), while SCD is like a historian meticulously documenting the changes to key attributes (dimensions) and their impact over time. CDC provides the raw data, while SCD uses that data (and other information) to maintain the historical context of the data warehouse.
