Introduction to the Is Type Check
The Is Type check asserts that every value stored in a text field can be read as a chosen data type: a number, a boolean, a date, or a timestamp. Use it on columns that arrive as text but must carry typed data, such as a CSV feed or a staging table.
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 accepted types, matching semantics, NULL handling, 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 the type, staging text feeds, 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 an Is Type check on a text field.
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Edit a Check
Step-by-step tutorial for changing the expected type, or adjusting the filter, coverage, and 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 formats, NULLs, arrays, and anomaly reporting, and how to resolve them.
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API
Payload shape and field notes for creating an Is Type check programmatically.
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FAQ
Short answers to questions about accepted types, formats, NULLs, and anomaly reporting.