Introduction to the Data Diff Check
The Data Diff check compares a target container against a reference container row by row and reports every row that was added, removed, or changed between the two. Use it to validate a replica or migration, reconcile a delivery with the system of record, or detect drift on a derived copy.
This section is the complete guide to the check. The Deep Dive covers the definition, properties, and full evaluation semantics, explains how anomalies are reported, walks through production examples, and collects best practices; the How-tos are step-by-step tutorials for creating, editing, and deleting a check; the API page documents the payload for programmatic use; and the FAQ answers the most frequent questions.
Next Steps
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How It Works
The complete reference: definition, field scope, properties, diff statuses, Row Identifiers, Comparators, and the filter clauses.
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Anomaly Reporting
The anomaly message the check produces, what the counts mean, and how differing rows appear side by side.
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Examples
Three production scenarios with sample data, anomaly messages, and the resulting comparison view.
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Best Practices
Guidelines for choosing Row Identifiers, tuning Comparators, scoping both sides, and keeping the cost down.
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Permissions
The team permission each action needs: view, create, edit, archive, restore, and delete.
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Create a Check
Step-by-step tutorial for creating a Data Diff check between two containers.
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Edit a Check
Step-by-step tutorial for changing the compared fields, Row Identifiers, reference container, Comparators, or filters.
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Delete a Check
Step-by-step tutorial for archiving a check and deleting it permanently.
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Troubleshooting
Common problems with row pairing, tolerated differences, scope mismatches, and cost, and how to resolve them.
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API
Payload shape and field notes for creating a Data Diff check programmatically.
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FAQ
Short answers to questions about Row Identifiers, Comparators, change types, and anomaly reporting.