Data Monitoring / Pipeline Freshness & Data Quality
Data pipelines: Referential integrity gaps
Facts arrive before their required dimension records. Count unmatched foreign keys using a consistent publication boundary.
Environment and inputs: Data pipelines and analytical datasets; Apache Airflow is an orchestration example. Quality acceptance rules must match the dataset contract.
Failure scenario
Facts arrive before their required dimension records. Confirm the cause before choosing a fix.
Evidence to collect
Count unmatched foreign keys using a consistent publication boundary. Record the time, affected scope and a healthy comparison interval.
Fix the confirmed cause
Coordinate load ordering or apply an explicit late-dimension policy and verify eventual reconciliation. Write the expected result first. Change one relevant setting at a time.
Check the result and hand over
Repeat the check for referential integrity gaps 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.