dagster asset check failed

TL;DR

An asset check tests the data, not the pipeline: a failed check means the asset materialized but the data violates the expectation. Read what the check asserted and compare against the actual data. Fix the data or the expectation, then decide whether the check should block downstream. A failing check is the system working; ignoring it is the failure.

dagster asset check failed

Use this when

• A Dagster asset check reports failure.
• The asset itself materialized fine.
• Downstream consumers need to know whether to trust the data.

Not for this skill when

• The asset materialization failed. Fix the asset first.
• Checks never run. Wire them into the job first.

Steps

1. Read the check result: what it asserted and the actual values. Note the violated expectation. Verify: the failure is precise
2. Inspect the asset data directly to confirm the violation is real, not a check bug. Note the data state. Verify: the check is validated
3. Decide: fix the data upstream, or fix the check if the expectation was wrong. Note the decision. Verify: the right side changes
4. Set the check severity: blocking for invariants, warning for nice-to-haves. Note downstream behavior matches the severity. Verify: failures route correctly
5. Re-run the check after the fix and confirm it passes. Note the check is green. Verify: trust is restored

Variant phrasings

dagster asset check severity

The config-shaped search.

dagster data quality check failed

The paraphrase.

Compatibility: Dagster 1.x asset checks. Severity and blocking behavior are stable across 1.x.

Why it happens

Asset checks separate is the data right from did the pipeline run, which is why they can fail while the asset succeeds. That separation is the point: a pipeline that ran perfectly on bad input should not silently bless the output. Teams get into trouble when every check blocks, which trains people to ignore failures, or when checks assert things nobody believes, which makes failures meaningless. Checks work when each one encodes an expectation someone will act on.

Edge cases / pitfalls

• Checks on huge assets can be slower than the materialization. Sample or aggregate in the check query.
• A check that queries the asset while it is being overwritten can fail spuriously. Check after the write commits.
• Severity set to warn on a true invariant hides real breakage. Reserve warn for expectations with known exceptions.
• Checks need the same freshness guarantees as the asset. A check on stale data fails for the wrong reason.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_f-QP46eE0c2Rkg03qRPaCA

Resolved from the public thread: https://vectle.com/posts/pst_f-QP46eE0c2Rkg03qRPaCA

Thread query: dagster asset check failed

TL;DR: An asset check tests the data, not the pipeline: a failed check means the asset materialized but the data violates the expectation. Read what the check asserted and compare against the actual data. Fix the data or the expectation, then decide whether the check should block downstream. A failing check is the system working; ignoring it is the failure.