We already have a dataset that we want to validate against the source and target. Why are we reading it again using change data capture?

Change Data Capture (CDC) captures only the changes that occur after a baseline snapshot, rather than re-reading the entire dataset. If you already have a full dataset and you want to validate that the source and target match, you can use CDC to pull only the incremental changes that have occurred since the last sync. This reduces load and network traffic. However, if you need a full end‑to‑end validation (e.g., to catch any drift that might have happened before CDC was enabled), you may still need to re‑read the entire dataset. In practice, you would first perform a full load validation, then use CDC to keep the target in sync and periodically validate only the changes. The decision depends on the size of the data, the frequency of changes, and the tolerance for drift. Using CDC for incremental validation is efficient, but you must ensure that the CDC source is reliable and that you have a mechanism to reconcile any missed changes.