/authoring-data-quality-checks

name: authoring-data-quality-checks
description: >
  Adds and runs data quality checks (dbt-test style assertions) on a project's warehouse tables and
  saved-query views: not-null, uniqueness, accepted values, referential integrity, row-count bounds,
  freshness, and custom HogQL. Use when asked to test a model, validate a view, check for nulls or
  duplicates, add data quality checks, find out why a number looks wrong, or judge whether a warehouse
  table is trustworthy before using it in an analysis. To describe what data *means* (metrics,
  certifications, joins), see setting-up-data-catalog instead. Trigger terms: data quality, data test,
  dbt test, not null check, uniqueness check, freshness check, referential integrity, row count check,
  validate model, is this table trustworthy.

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.informati