Since I haven't heard about your hands‑on experience yet, can you explain how you typically debug and troubleshoot failures in Databricks notebooks during ETL processes?

When a Databricks notebook fails, I follow a systematic approach. First, I examine the cell that raised the exception and read the full stack trace in the notebook UI. I then check the Spark UI for the corresponding job, stage, and task details to identify whether the failure is due to data issues, resource limits, or code bugs.

Next, I use dbutils.notebook.exit and display to surface intermediate results, and I enable Spark’s spark.sql.autoBroadcastJoinThreshold and spark.sql.shuffle.partitions to tune performance if the failure is related to shuffles. I also inspect the cluster logs in the "Driver Logs" and "Executor Logs" tabs for out‑of‑memory or serialization errors.

If the issue is data‑specific, I run a small sample job with sample(0.01) to reproduce the error locally. For configuration problems, I review the cluster’s init scripts and environment variables. Finally, I document the root cause and fix in a JIRA ticket, and I add unit tests or data quality checks to prevent recurrence.