Semantic layers are finally getting opinionated enough to be useful

A semantic layer isn't new; it's the "business translation" between raw data and real decisions. AI makes it necessary now.

40% of Databricks users still don't use dbt. Each BI tool in your org has its own definition of "revenue." There are dozens of dashboards, but none of them line up. What happened?

AtScale, Stardog, Databricks Unity Catalog Metrics, and other semantic layers fix this by defining metrics once and making them usable in SQL, DAX, MDX, Python, and even AI agents.

The secret isn't "no-code BI." It's no-drift semantics: metrics mean the same thing to analysts, ML engineers, and LLMs.

Your dashboards and model training data should both use the same "revenue" metric.

The AtScale + Databricks "Semantic Lakehouse" model gets this right:

• No moving data
• Automatic aggregates
• Unified metric definitions
• Direct integration with Unity Catalog and Spark

It gives AI a stable source of business truth.