Medallion architecture (bronze, silver, gold) is easy to draw on a whiteboard and easy to get wrong in practice - the hard part is deciding what transformation logic belongs at each layer and how to track model experiments across a pipeline that's constantly reprocessing data.

This is a hands-on build: I'll construct a full medallion pipeline on Azure Databricks using Apache Spark for the bronze-to-gold transformations and MLflow for experiment tracking and model versioning at the gold layer, covering schema evolution as raw data changes, incremental processing so you're not reprocessing your entire bronze layer on every run, and wiring MLflow tracking into the pipeline so model training stays reproducible as the underlying data evolves. Real code, real data, real mistakes included.