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.

Reach for these first, in roughly this order:

• not_null on the columns downstream joins and filters depend on. The single highest-value check. A null join key silently drops rows.
• unique on whatever the model claims is its grain. If orders is one row per order, say so.
• relationships on foreign keys. Catches the join that quietly stopped matching after an upstream change.
• accepted_values on status and category columns whose downstream logic branches on them.
• freshness on the timestamp column of anything that syncs. Catches a dead pipeline, which no row-level check will.
• row_count bounds when you know the plausible range. Good for catching a truncated sync.
• custom_sql only when nothing above expresses the invariant — e.g. cross-column arithmetic (select 1 from orders where total != subtotal + tax). Every row it returns counts as a failure.

Call posthog:data-quality-check-types for each type's exact config schema rather than guessing.

Checks live on the subject they audit: create them with data-quality-check-create-on-view (saved_query_id path parameter) or data-quality-check-create-on-table (table_id).

Severity and triggers

Severity is a decision about consequences, not about confidence. Use error when the failure means downstream numbers should not be trusted — those failures mark the subject failing and notify. Use warn for things worth surfacing that nobody would act on today. When unsure, warn is the safer default: an error check that cries wolf gets everything ignored.

Triggers — there is nothing to schedule. A check runs when its subject's data changes: a materialized view's checks run as part of its refresh (and, when the team turns the gate on, a refresh whose error-severity checks fail is not published), a source table's checks run after each completed sync, and a plain view's checks run when its DAG runs. Checks on a view outside any DAG only run on demand.

Verify what you wrote

Author, run once, read the result. A check nobody has run is a guess.

1. posthog:data-quality-check-create-on-view (or -on-table)
2. posthog:data-quality-check-run-on-view (or -on-table) — returns a suite run
3. Poll system.information_schema.data_quality_check_runs (or posthog:data-quality-check-results-on-view/-on-table) for the outcome

A failed result on the first run is the interesting case: either you found real bad data, or the assertion is wrong. Take the compiled_query off the run, execute it with posthog:execute-sql, and look at what it actually matched before reporting anything. That compiled_query comes from posthog:data-quality-check-results-on-view/-on-table; the information_schema poll in step 3 does not return it. An errored result is never a data problem — the query could not run at all, usually a column name typo or a subject that no longer exists.

Judging a source before you use it

When an analysis depends on a warehouse table or view, check its verdict first:

SELECT subject_name, health, checks_total, checks_failing, last_run_at
FROM system.information_schema.data_quality_health

• failing — an error-severity check found bad data. Say so in your answer; don't quietly use it.
• erroring — a check couldn't run. The data may be fine, but nobody is watching it.
• warn — only warn-severity failures. Usable, worth a mention.
• healthy — checks ran and passed.
• unknown / absent — no checks, or none have run. Absence of failures is not evidence of health.

For the history behind a verdict, system.information_schema.data_quality_check_runs carries recent executions with observed_value recorded on passes too, so you can see when a number started drifting rather than just that it is wrong now.

Related

• setting-up-data-catalog — what the data means: metrics, trust marks, relationships.
• querying-posthog-data — the schema-discovery and HogQL rules these queries follow.