The Medallion Architecture Debate: Why Nobody Agrees on Silver vs. Gold
Exploring the hidden inconsistencies in how teams implement raw-silver-gold layering models, from transformation logic disagreements to the platinum layer controversy.
Ask ten data engineers to define the Silver layer in medallion architecture, and you’ll get eleven different answers. This familiar framework that promised standardization has become a breeding ground for interpretation drift, tribal knowledge, and heated debate about what actually belongs where.
The medallion architecture’s simple premise ↗ of Bronze (raw), Silver (cleaned), and Gold (aggregated) layers is both its biggest strength and most significant weakness. Everyone understands the general concept, but the devil is in the details, specifically, which details belong to which layer.
Where Standardization Falls Apart: The Silver-Gold Border Wars
The fundamental disagreement isn’t about Bronze, nearly everyone agrees that’s the raw data dumping ground. The confusion starts when teams try to draw lines between Silver and Gold.
One Reddit commenter captured the essence perfectly: “There are no hard and fast rules about where these lines exist.” Some teams consider Silver to be purely about data cleaning and basic standardization, while others pack it with business logic and domain modeling. The parallels to historical database design are striking, many implementations resemble the traditional staging/ODS, DW and Data Marts layers that have been around for over 25 years.
The most elegant definition I’ve encountered frames the layers as transitions between fidelity: Bronze maintains source fidelity, Silver achieves unification and standardization across sources, and Gold focuses on presentation fidelity.
But elegant definitions don’t solve implementation arguments.
The Transformation Tug-of-War
The core debate centers on transformation responsibility. Should Silver handle complex joins and business logic, or should it remain minimal, limited to deduplication, type casting, and basic validation?
Teams implementing Silver with heavy transformations argue it creates a “single source of truth” for the entire platform. Others push complex transformations to Gold, keeping Silver lean and reusable. The Databricks documentation supports this flexibility, noting that Silver typically handles data cleansing, filtering, and standardization while Gold focuses on aggregation and applying business logic to create KPI-driven datasets.
Do We Need a Fourth Platinum Layer?
When teams can’t agree on Silver vs. Gold responsibilities, some create compromise solutions that only add to the confusion. The “Platinum layer” proposal typically emerges in one of two scenarios:
- Pre-Gold Refinement: Teams want a layer for intermediate modeling before final business metrics
- Post-Gold Enhancement: Teams need additional enrichment for specialized use cases
The problem? This just kicks the can down the road. Now you have four layers to define instead of three, with even more ambiguous boundaries.
A more practical approach some teams adopt is treating Silver as “cleaned but not modeled” data, while Gold contains “modeled but not aggregated” data. This leaves room for a potential Platinum layer for highly specialized, consumption-ready datasets.
Implementation Inconsistencies Across Platforms
The medallion concept translates differently across data platforms. In Databricks implementations ↗, Bronze typically stores raw data in Delta Lake format, Silver applies cleansing and transformation using Apache Spark, and Gold creates analytics-ready datasets optimized for tools like Power BI and Tableau.
Contrast this with DuckDB implementations ↗ where the same Bronze → Silver → Gold pattern serves local development needs with file-backed and in-memory modes. Here, the lines blur even further as teams balance persistence needs with development agility.
The Real Purpose: Signposts, Not Standards
Perhaps the most insightful perspective comes from recognizing that these labels serve users, not engineers. As one data practitioner noted, “The labels are for users, not engineers. Where do you go for business ready data? What if you want curated and clean but not yet aggregated with metrics?”
The medallion layers function as wayfinding tools rather than rigid technical specifications. They answer fundamental questions:
- Bronze: “Where’s the raw stuff I can experiment with?”
- Silver: “Where’s the clean data before business rules?”
- Gold: “Where are the finished reports and dashboards?”
When teams treat these layers as communication tools rather than architectural mandates, the inconsistencies become features, not bugs.
Practical Resolution: Strategy Over Dogma
After examining dozens of implementations, the most successful teams share common practices that transcend layer definitions:
Define Transformation Contracts: Establish clear contracts between layers. Bronze guarantees data integrity and schema evolution capabilities. Silver guarantees data quality standards and consistent formatting. Gold guarantees performance SLAs and business logic accuracy.
Embrace Pragmatic Variance: Different domains might need different interpretations. Customer data might require extensive Silver-layer cleansing, while IoT sensor data might flow directly from Bronze to Gold aggregations.
Document Your Interpretation: The most critical step is documenting your team’s specific interpretation. If Silver includes business logic in your organization, document it. If Gold is strictly for aggregated metrics, make that explicit.
The Bottom Line: Your House, Your Rules
The medallion architecture’s flexibility is its superpower, not its failure. The framework provides enough structure to prevent chaos while allowing teams to adapt to their specific needs.
The real problem isn’t the inconsistency, it’s the assumption that one-size-fits-all definitions should exist. Every organization has different data maturity, different use cases, and different definitions of “clean” versus “business-ready.”
Rather than searching for universal answers, focus on establishing internal consistency. Document your team’s interpretation, establish clear layer contracts, and remember that the best architecture is the one your team actually understands and uses effectively.
After all, a standardized mess is still a mess.
