Since you haven't yet shared details on re‑engineering business logic, can you explain your process for validating data in TaaS and performance after migrating legacy pipelines to Databricks?

I start by defining a data quality framework that captures key metrics: completeness, consistency, accuracy, and timeliness. For each business rule, I write unit tests using libraries like Great Expectations or dbt tests, and run them against both source and target datasets. I also set up automated data drift detection to flag deviations over time. For performance, I profile the legacy pipeline to establish baseline metrics (execution time, resource usage). In Databricks, I use Delta Lake to enable ACID transactions and schema enforcement, then run the same transformations with Spark UI to capture execution plans. I tune partitioning, broadcast joins, and caching based on the profiling results. Continuous integration pipelines trigger these tests on every code change, ensuring that business logic remains correct and performance stays within acceptable limits.