Data Monitoring / Pipeline Freshness & Data Quality
Data pipelines: Orchestrator scheduler health
No tasks start even though worker hosts are healthy. Inspect scheduler health, pending task state and dependency readiness separately.
Environment and inputs: Data pipelines and analytical datasets; Apache Airflow is an orchestration example. Quality acceptance rules must match the dataset contract.
Failure scenario
No tasks start even though worker hosts are healthy. Confirm the cause before choosing a fix.
Evidence to collect
Inspect scheduler health, pending task state and dependency readiness separately. Record the time, affected scope and a healthy comparison interval.
Fix the confirmed cause
Repair the orchestration bottleneck and confirm new scheduled work actually launches. Write the expected result first. Change one relevant setting at a time.
Check the result and hand over
Repeat the check for orchestrator scheduler health 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.