dbt Semantic Layer APIs put MetricFlow metrics behind governed query interfaces

The extract describes fragmentation of business logic as tools multiply, then positions the Semantic Layer as the place to define metrics in code with MetricFlow and to generate and query datasets from dbt-governed assets such as metrics and models. Integration is framed as a way to cut duplicate coding, keep development workflows lean, and keep consumers on consistent definitions.

For data and analytics teams, the practical claim is that BI, monitoring, privacy, cataloging, and ML workloads can share the same metric surface instead of re-encoding logic in each tool. The GraphQL path is called out specifically for querying metrics and dimensions downstream.

That pattern maps to shared, machine-readable meaning: metrics and models are treated as governed assets that many systems read from, rather than as local copies of business logic. Consistency for data consumers is presented as the governance outcome of that shared definition.