Snowflake shipped Semantic Views and a lot of people declared the category solved.
It's a real step forward. It's also scoped to exactly one warehouse.
What you genuinely get
Metric definitions living next to the data, governed by Snowflake's own model, queryable by Cortex and external tools. For a single-platform estate that beats redefining the same metric in four dashboards.
What to understand before building on it
| Consideration | Reality |
|---|---|
| Authoring | Hand-written by your team |
| Scope | Ends at the Snowflake boundary |
| Governance | Snowflake RBAC — strong, but Snowflake-only |
| Drift detection | None; a stale definition stays stale silently |
| Join proof | Not provided — inference still happens upstream |
The drift row is the one that bites over time. A definition that stopped matching how people query it produces no error, no alert, and no failing test. You find out when two teams disagree in a meeting.
The question that decides it
What happens to the third of your data that isn't in Snowflake?
For most enterprises there's a lakehouse someone piloted, an operational Postgres, and a SaaS system that never got migrated. A warehouse-native semantic layer can't see any of it, so cross-boundary joins fall back to hand-written SQL — and the metric gets defined a second time, differently.
The pragmatic position
Adopt Semantic Views. They're good, they're included, and they improve consistency inside Snowflake.
Just don't mistake a metric store for an execution layer. It tells an agent what a metric is; it doesn't prove the join is valid or that the caller was entitled to the rows.
The full breakdown — what Semantic Views do in detail, the boundaries, and what a cross-estate layer adds above them — is here:
👉 Snowflake Semantic Views Explained: What They Are, and What Sits Beyond
Originally published at colrows.com/blogs/snowflake-semantic-views
Top comments (0)