authoring-data-quality-checks

Authoring data quality checks

A check is one assertion about one warehouse table or view. It compiles to a count-only HogQL query and passes when it finds zero failing rows — the same semantics as dbt test. Failing rows are never stored; only counts and the compiled query are, so to see the offending rows you re-run the stored query yourself.

row_count is the exception. It passes when the observed count is within its configured min/max bounds, so its failed_row_count comes back null and its stored query returns that single count, not offending rows. Read the observed count to judge it rather than looking for matched rows.

Reads go through SQL (system.information_schema.data_quality_*); writes and runs go through the data-quality MCP tools.

Before you write anything: look

Two queries save you from the two most common mistakes — duplicating a check, and checking a column that doesn't exist.

-- What is already covered?
SELECT name, subject_name, column_name, check_type, config, severity, last_status
FROM system.information_schema.data_quality_checks
WHERE subject_name = 'orders'

-- What columns are there, and what do they mean?
SELECT column_name, data_type, description
FROM system.information_schema.columns
WHERE table_name = 'orders'

Re-creating a byte-identical check is a harmless no-op — checks are keyed by a fingerprint of the subject, type, column, and config, so an identical create upserts. A near-duplicate is not harmless: it doubles the noise for whoever reads the results. If an existing check's assertion is close but wrong, create the corrected check and delete the old one — the assertion (type, column, config) is immutable and the subject is fixed by the URL, so an update that tries to change them is rejected. Update is only for metadata, severity, and ownership.

Choosing checks

Aim for a handful that would actually catch a real regression, not blanket coverage. A model with twenty checks nobody reads is worse than three that fail meaningfully.