Data Reconciliation Recipe

The Data Reconciliation recipe is a guided flow around the existing Data Diff rule type. It is not a new rule type. The recipe helps you pair an asset you want to investigate (the left side) with a reference asset treated as correct (the right side), pick the row identifiers that match a row on one side to its counterpart on the other, choose the fields that must agree and how close is close enough, and creates an ordinary Data Diff check from them. It then scans every record, groups the mismatches for you to resolve, and writes the corrections as a keep set and a remove set to your enrichment destination.

The check the recipe creates is an ordinary quality check. It stays Active after the recipe finishes, so future scans keep reporting drift between the two assets, and their anomalies can bring you straight back into the recipe's Reconcile step.

This Deep Dive covers:

• The two phases, Build & Validate and Scan & Reconcile, and the steps inside each
• The prerequisites: shared fields with compatible types, and a linked enrichment destination on the left datastore before the Scan step
• What the run produces: the check, the scan, the anomaly, the keep set, and the remove set
• The entry points that pull an existing check or anomaly into the flow, and how progress is kept
• Examples, best practices, and the permissions behind each step

For the rule's own semantics, such as how rows are paired and how tolerances are applied, see How Data Diff Checks Work.

What You Will Find Here