Slowly Changing Dimensions (SCD)
SCD is a method used to track and manage changes in data over time while preserving the historical version of that data. It ensures that past data is not lost when updates occur.
How It Works:
- When data changes (e.g., a customer's address), instead of overwriting the old data, a new record is added or updated in a specific way to preserve the history.
- There are different types of SCD implementations:
- SCD Type 1: Overwrite the old data (no history kept).
- SCD Type 2: Keep historical records by adding new rows with versioning or timestamps.
- SCD Type 3: Add a limited history by keeping changes in additional columns.
When to Use:
- When you need to maintain historical records of data for auditing, reporting, or analysis.
- Commonly used in data warehouses or systems where past data trends are important (e.g., financial or compliance reports).
Change Data Capture (CDC)
Definition:
CDC is a method used to identify and capture changes (inserts, updates, deletes) in a source system and then apply those changes to a target system.
How It Works:
- CDC detects changes made to a database (e.g., through logs or triggers) and extracts only the modified records, avoiding the need to reload the entire dataset.
- Changes are typically applied to the target system in near real-time or batch processes.
When to Use:
- When you need to keep a target system (e.g., a data warehouse or application) in sync with a source system.
- Ideal for real-time or incremental data processing, where performance is critical, and reloading the full data is impractical.
Key Differences Between SCD and CDC
| Aspect | SCD (Slowly Changing Dimensions) | CDC (Change Data Capture) |
|---|---|---|
| Purpose | Maintain historical data. | Detect and transfer only changes. |
| Focus | Tracks changes over time with history. | Focuses on capturing and applying real-time updates. |
| Data Handling | Adds new records or updates existing ones with history. | Extracts only changed records from the source. |
| Use Case | For historical analysis, reporting, or auditing. | For syncing data between systems efficiently. |
| Frequency | Periodic updates based on business needs. | Near real-time or incremental updates. |
| Example | Tracking a customer’s address history. | Detecting changes to customer records in real-time. |
Summary
- SCD is about preserving historical data changes for long-term analysis.
- CDC is about identifying and moving only the changes to keep systems in sync.
Both are complementary and often used together: CDC can detect changes, and SCD can store those changes with historical context.
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