Most people assume Tesla is a car company. People like Mr. Wonderful (Kevin O'Leary) recognize Tesla is a data company. The more data you acquire and then learn from, the better you can build your business and improve your processes.
The three levels of Medallion Architecture are below.
Bronze (Raw): Data ingested directly from source systems, with minimal or no transformation.
Silver (Refined): Cleaned, structured, and enriched data that’s ready for broader analysis. At least one transformation has occurred to boost data value.
Gold (Business-ready): Aggregated, curated data models optimized for reporting, dashboards, and decision-making. Commonly stored in Data warehouses for optimized and efficient analytics.
So why are data teams increasingly adopting the medallion architecture model? The appeal lies in bringing structure and clarity to rapidly evolving data environments. By organizing data into layered stages teams create a clear, repeatable framework for managing data transformation and access.
The benefits usually look like this:
Clarity: Distinguishes raw data from analysis-ready data
Role alignment: Engineers focus on bronze/silver; analysts work with gold
Separation of concerns: Minimizes risk by isolating raw data from transformations
Scalability: Provides repeatable patterns as data volume grows
For many teams, this is a useful abstraction that brings order to the complexity of a modern data stack. But as always, the theory doesn’t always match practice.
Organizing data into structured layers helps every consumer of data know where to look for the information that will be most beneficial to their efforts.