What is the Medallion Architecture, and can you explain your experience with it and the purpose it serves?

The Medallion Architecture is a layered approach to building a data lake that separates raw, curated, and refined data into Bronze, Silver, and Gold layers. In the Bronze layer, I ingest raw, unstructured or semi‑structured data from sources such as Kafka, S3, or APIs and store it in its native format. The Silver layer cleanses and enriches the data, applying transformations, schema enforcement, and data quality checks; this is where I typically use Spark or Databricks notebooks to perform joins, deduplication, and type casting. The Gold layer contains business‑ready tables that are aggregated, denormalized, and optimized for analytics; these tables are often materialized as Delta tables or Parquet files partitioned by business keys. My experience includes designing these layers in a Delta Lake environment, implementing automated pipelines with Airflow or Databricks Jobs, and enforcing governance through Unity Catalog. The purpose of the Medallion Architecture is to provide a scalable, auditable, and maintainable foundation that allows data consumers to access the right level of data quality for their use case while keeping the raw source intact for future reprocessing.