Data Monitoring / Pipeline Freshness & Data Quality
Data pipelines: Task success versus output acceptance
A transformation exits successfully with an incomplete output partition. Compare task status with expected files, row counts and published batch markers.
Environment and inputs: Data pipelines and analytical datasets; Apache Airflow is an orchestration example. Quality acceptance rules must match the dataset contract.
Failure scenario
A transformation exits successfully with an incomplete output partition. Confirm the cause before choosing a fix.
Evidence to collect
Compare task status with expected files, row counts and published batch markers. Record the time, affected scope and a healthy comparison interval.
Fix the confirmed cause
Make output acceptance a separate observable step and block downstream publication when it fails. Write the expected result first. Change one relevant setting at a time.
Check the result and hand over
Repeat the check for task success versus output acceptance using the same scope and a representative interval. Check that the symptom is resolved, expected records are present and downstream results are correct. If coverage, correctness or latency worsens, restore the recorded configuration through the agreed rollback process.
Scope and precautions
Prefer read-only access and limit queries to the relevant period or dataset. This example has not been tested on your systems. Test changes in an approved environment. Keep credentials and sensitive data out of shared evidence.