Within an ETL pipeline, how do you handle error calls?

Error handling in an ETL pipeline is a combination of defensive coding, monitoring, and recovery strategies. First, wrap each transformation step in a try‑catch block or use the framework’s built‑in error handling (e.g., Spark’s DataFrameWriter with mode('error')). Log the exception with context (source, row key, timestamp) to a centralized log store. For transient errors, implement retries with exponential back‑off. For data quality violations, route the offending rows to a separate "error" or "dead‑letter" table so the main pipeline can continue. Use schema validation libraries (e.g., Great Expectations) to catch schema drift early. Finally, set up alerts (Slack, PagerDuty) for critical failures and schedule automated remediation jobs that can re‑process failed batches. This layered approach ensures that the pipeline is resilient, auditable, and recoverable.