Data Monitoring / Pipeline Freshness & Data Quality
Data pipelines: Expected batch arrival
A daily batch is absent while all completed task runs look successful. Compare an expected schedule calendar with observed batch identifiers.
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 daily batch is absent while all completed task runs look successful. Confirm the cause before choosing a fix.
Evidence to collect
Compare an expected schedule calendar with observed batch identifiers. Record the time, affected scope and a healthy comparison interval.
Fix the confirmed cause
Detect missing arrivals explicitly and account for holidays or approved skipped periods. Write the expected result first. Change one relevant setting at a time.
Check the result and hand over
Repeat the check for expected batch arrival 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.