Data Monitoring / Pipeline Freshness & Data Quality
Data pipelines: Timezone and daylight-saving boundaries
A daily aggregate loses or duplicates an hour during a clock change. Compare UTC event storage with local business-period boundaries and source timezone metadata.
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 aggregate loses or duplicates an hour during a clock change. Confirm the cause before choosing a fix.
Evidence to collect
Compare UTC event storage with local business-period boundaries and source timezone metadata. Record the time, affected scope and a healthy comparison interval.
Fix the confirmed cause
Define period conversion centrally and test both short and long local days. Write the expected result first. Change one relevant setting at a time.
Check the result and hand over
Repeat the check for timezone and daylight-saving boundaries 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.