Can you describe how you analyze and map legacy ETL workflows when migrating them to Databricks? What steps do you follow to ensure a smooth transition?

I begin with an inventory of all legacy jobs, capturing source systems, schedules, and dependencies. Data profiling tools (e.g., Informatica, Talend) help me understand cardinality, data quality, and transformation logic. I then create a mapping document that translates each transformation into Spark SQL or PySpark code, noting any changes needed for performance or scalability. Next, I build unit tests for each transformation using frameworks like dbt or PyTest, and run them against sample data. Integration tests validate end‑to‑end pipelines, ensuring that data flows correctly through all stages. I perform incremental migration, moving one job at a time, and use Delta Lake to guarantee ACID compliance. Performance benchmarks compare execution times and resource usage against the legacy baseline, and I tune partitioning, caching, and cluster sizing accordingly. Finally, I set up monitoring dashboards (Grafana, Datadog) and alerting to detect anomalies, and I maintain a rollback plan for each job to ensure a smooth transition.