Why do we need to go back to CDC for mapping validation when we already have millions of records and are validating them in the raw zone?

CDC (Change Data Capture) captures incremental changes in the source system. When you perform mapping validation, you need to ensure that the transformations applied to the raw data produce the correct target records. Even if you have millions of records already loaded, the mapping logic may change over time, or new business rules may be added. By re‑reading the CDC stream, you can replay only the changed rows and validate the mapping against the latest source data without reprocessing the entire dataset. This approach is efficient because CDC provides a lightweight, incremental view of changes, and it guarantees that you are validating against the exact state of the source at the time of change. Additionally, CDC logs often include metadata such as timestamps and operation types (insert, update, delete), which help in verifying that the mapping correctly handles all scenarios. Therefore, going back to CDC for mapping validation ensures consistency, reduces processing time, and keeps the validation pipeline aligned with the source system’s current state.