How to implement a scalable Medallion Architecture using Databricks and Delta Lake?

All answers to this question.

Does implementing a Medallion structure significantly increase storage costs due to the data being replicated across three different tiers?

   Answered 2025-01-05 by Thomas Wright

• Thomas, while storage costs do increase, the trade-off is often worth it for the reliability and speed of the Gold layer. To optimize, you should set aggressive lifecycle policies on your Bronze tier. Many teams move Bronze data to "Cold Storage" like AWS Glacier or Azure Archive after 30 days. This keeps your active Delta Lake performance high while keeping the "insurance" of raw data available for re-processing at a fraction of the cost.

     Commented 2025-01-15 by David Miller

We found that using Delta Live Tables (DLT) simplified the Medallion setup. It handles the orchestration and data quality checks automatically between tiers.

   Answered 2025-02-02 by Linda Robinson

• I agree completely, Linda. DLT is a game-changer for monitoring. Being able to see the data quality metrics in real-time as data flows from Silver to Gold is invaluable.

     Commented 2025-02-10 by Mark Stevenson

The Medallion Architecture is ideal for creating a clean data flow. In the Bronze layer, keep your data raw—just a dump of the source with metadata like ingestion timestamps. The Silver layer is where you handle schema enforcement, deduplication, and initial cleaning. For the Gold layer, the focus is on business-level aggregates. To ensure idempotency, I highly recommend using MERGE INTO operations in Delta Lake. This allows you to handle late-appearing data or reprocessing without creating duplicates. In a project I led in late 2023, we used dbt (data build tool) on top of Databricks to manage the SQL transformations for the Gold tier, which provided a clear lineage for our stakeholders and made debugging significantly easier.

   Answered 2025-12-22 by Karen Mitchell