How do you handle failures in your data pipeline? What is your approach to monitoring and troubleshooting?

To handle failures in a data pipeline, I follow a three‑step process: alert, diagnose, and fix & rerun. First, I set up real‑time alerts using CloudWatch alarms or Airflow callbacks so the team is notified immediately when a task fails. This reduces mean time to recovery. Second, I diagnose the root cause by inspecting task logs and the source data. In my last project, most failures were due to schema drift or missing partitions in the source S3 bucket, not code bugs. I use tools like AWS Glue to detect schema changes and maintain a schema registry. Third, I fix the issue—whether that means updating the schema, adding missing partitions, or patching code—and then rerun the failed task. I also document the incident and update monitoring dashboards to prevent recurrence. This approach ensures quick detection, accurate root‑cause analysis, and efficient remediation, keeping the pipeline running smoothly.