From Silver to Gold: Why the Semantic Layer Is the Future of Data Product Engineering
Modern data environments are becoming more complex. Data lives in cloud data warehouses and across lakes, SaaS platforms, real-time streams, and operational systems.
The standard way to build an enterprise data lakehouse is to use the medallion architecture, where data is taken from the source system and transformed in stages. These stages are referred to as bronze, silver, and gold layers. The approach offers several benefits, including improved data quality, easier data governance, enhanced performance, and increased agility.
By organizing data into three distinct layers, it facilitates a structured and intuitive approach to data processing, making it easier to onboard new team members and scale data workflows. As organizations scale, so does the challenge of turning this sprawling web of raw information into clear, actionable insights.
Traditionally, organizations relied on ETL pipelines, well-modeled warehouse and star schemas, and models such as Snowflake to make sense of this data. These structures form what many call the “silver layer” in data lakehouse architectures where data is cleaned and structured and query-ready datasets are created. But while these models are essential, they’re not enough when they stand alone. The final leap to delivering consistent, business-ready insights, the gold layer requires something more.
That “something” is the semantic layer. Acting as a bridge between an organization’s data infrastructure and the tools that consume it, the semantic layer defines and manages business logic, metrics, and data relationships in a centralized, reusable way. It empowers teams to work from a shared source of truth, regardless of whether they’re building dashboards, training AI models, or performing ad hoc analysis.
THE PROBLEM: DATA FRAGMENTATION AND METRIC DRIFT
In an ideal world, every team would work from the exact definition of core business metrics. However, in practice, the reality is far messier. As data proliferates across warehouses, lakes, APIs, and third-party platforms, each team creates its interpretation of key terms, whether it’s “active users,” “monthly revenue,” or “customer churn.” These definitions often live in BI tools, notebooks, or custom scripts, disconnected from each other and the source systems. Across time, this leads to what many call metric drift, a situation in which different teams report different numbers for what should be the same metric.
This problem isn’t new, as organizations have tried for years to formalize shared definitions by embedding business logic into star schemas, Snowflake models, or views in the data warehouse. However, these well-modeled “silver” layers lack the business context and shared governance needed to create truly reusable data products. The result is a sprawl of siloed definitions and duplicated logic across teams and tools.
This is where the concept of the gold layer comes in. Here, the goal is to create a curated version of an organization’s data enriched with business meaning, standardized metrics, and shared terminology. Conceptually, this gold layer is the semantic layer. It’s not just about data that has been cleaned, but data that speaks the language of the business.
The problem is that most organizations have treated this semantic definition layer as something informal or embedded deep within specific tools. Consistency breaks down without a central way to manage and expose this logic. To build trust in data and scale insight across teams, organizations need a new kind of abstraction that aligns business understanding with technical reality, one that can sit above the silver layer and unify how data is consumed. This is the role the semantic layer is now poised to play.