Our current Redshift setup is getting expensive due to massive unstructured data growth. We are looking into a Lakehouse model using Databricks or Snowflake. For those who made the jump, does the "Medallion Architecture" (Bronze/Silver/Gold) actually simplify governance, or does it just add more layers of ETL complexity?
3 answers
Migrating to a Lakehouse is less about "simplifying" and more about "scaling cost-effectively." In a traditional warehouse, you're paying a premium for storage and compute being tightly coupled. With a Lakehouse, specifically using the Medallion Architecture, you gain a clear lineage. The Bronze layer acts as your raw landing zone, Silver is for deduplicated/cleansed data, and Gold is for business-level aggregates. This separation actually improves governance because you can apply different access controls at each stage. We saw a 30% reduction in compute costs by offloading heavy transformations to the Silver layer using Spark before final ingestion into Gold.
Are you more concerned about the initial refactoring of your SQL scripts into Spark/Python, or is the team's ability to manage a Delta Lake or Iceberg format the bigger bottleneck?
The Medallion approach is great for debugging. If a dashboard in the Gold layer looks wrong, you can trace it back to the exact file in Silver to see where the logic failed.
I agree with Michael. Data lineage is the unsung hero of the Medallion pattern. It saves hours of "data archeology" when a pipeline breaks in the middle of a sprint.
Ryan, the skill gap is definitely the hurdle. To answer your point, we mitigated this by using dbt (Data Build Tool) on top of our Lakehouse. It allowed our SQL-heavy analysts to still build the Silver and Gold layers without needing to become PySpark experts. This way, we kept the Medallion structure intact while leveraging the "Iceberg" table format for ACID transactions, which gave us the reliability of a warehouse with the price tag of cloud storage.