Introduction to the Required Values Check
The Required Values check asserts that every value on a list actually shows up in a column at least once. It is the completeness rule for reference data: it does not care what else the column holds, only that nothing on your list went missing.
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, the value list, how it differs from Expected Values, the distinct-value limit, and why coverage does not apply.
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Anomaly Reporting
The Shape Anomaly the check produces, what it reports, and why there are no source records to inspect.
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Examples
Three production scenarios with sample data, the resulting anomaly, and the SQL equivalent of what the check evaluates.
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Best Practices
Guidelines for choosing the list, scoping with a filter, and pairing the rule with Expected Values.
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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 Required Values check on a field.
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Edit a Check
Step-by-step tutorial for changing the value list or the filter on an existing check.
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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 whitespace, casing, high-cardinality columns, filters, and aggregate anomaly reporting.
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API
Payload shape and field notes for creating a Required Values check programmatically.
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FAQ
Short answers to questions about the value list, how the rule differs from Expected Values, and anomaly reporting.