Semantic layers are finally getting opinionated enough to be useful

The semantic layer has been around for years. It's the business translation sitting between raw data and the decisions people actually make. What's changed is that AI now makes it necessary.

Look at the state of things. 40% of Databricks users still don't use dbt. Every BI tool in the org carries its own definition of "revenue." You end up with dozens of dashboards and none of them agree.

AtScale, Stardog, Databricks Unity Catalog Metrics, and the rest fix this by letting you define a metric once and then use it everywhere: SQL, DAX, MDX, Python, even AI agents. The point was never no-code BI. It's no-drift semantics, where a metric means the same thing to analysts, ML engineers, and LLMs. Your dashboards and your model training data should both pull from the same "revenue."

The AtScale plus Databricks "Semantic Lakehouse" model gets this right. No moving data around, automatic aggregates, one set of metric definitions, and direct integration with Unity Catalog and Spark. That gives AI a stable source of business truth to stand on.