Introduction to the Not Null Check
The Not Null check asserts that every selected field holds a value on every row. It is the most direct completeness rule in the platform: pick the columns that must always be populated, and any row where one of them is empty is reported.
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, multi-field behavior, empty strings, arrays, filter behavior, and coverage.
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Anomaly Reporting
The anomaly messages the check produces, what the numbers mean, Source Records highlighting, and Custom Anomaly Description.
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Examples
Three production scenarios with sample data, anomaly messages, and the SQL equivalent of what the check evaluates.
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Best Practices
Guidelines for choosing mandatory fields, handling empty strings, pairing rules, and keeping the signal clean.
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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 Not Null check on one or more fields.
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Edit a Check
Step-by-step tutorial for adding or removing fields, or changing the filter, coverage, or organizational properties.
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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 empty strings, multi-field checks, arrays, and anomaly reporting, and how to resolve them.
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API
Payload shape and field notes for creating a Not Null check programmatically.
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FAQ
Short answers to questions about empty strings, multiple fields, arrays, and anomaly reporting.