If something fails in the middle of a data pipeline, how do you detect it and where do you start troubleshooting? What did you need to set up before the components could communicate with each other?

When a failure occurs in the middle of a data pipeline, the first place to look is the orchestrator or workflow engine that schedules the jobs (e.g., Airflow, Prefect). Check the task status and logs for error messages. If the job ran but produced incomplete output, inspect the downstream component’s logs and metrics. Use monitoring dashboards (Prometheus, CloudWatch) to see if any metrics spike or alerts fire. Verify network connectivity between services, authentication tokens, and that the data format matches expectations. If the failure is in a transformation step, run the step locally with a sample payload to reproduce the error. Once you isolate the failing component, you can roll back to a known good state or apply a patch. Before components can talk to each other, you need to set up service discovery, secure communication (TLS), authentication (IAM roles, OAuth), and a shared schema registry or metadata store so each service knows the data format. These prerequisites ensure that when a failure occurs, you have logs, metrics, and a clear dependency graph to pinpoint the issue.