building-dbt-semantic-layer

To create a semantic model in dbt, add a semantic_model block to your model YAML, set enabled: true, and define your primary entities, dimensions, and metrics. You must also configure the agg_time_dimension to your primary time column for proper metric aggregation.

The dbt semantic layer is used to define consistent business metrics, dimensions, and semantics directly within dbt models using MetricFlow. It enables data teams to expose standardized metrics across different analytics tools, ensuring a single source of truth for reporting.

Validate dbt semantic layer configurations by running dbt parse to check YAML syntax, followed by mf validate-configs for local MetricFlow validation or dbt sl validate in dbt Cloud to ensure your semantic models, entities, and metrics are correctly structured.

Yes, a time spine is required when building dbt semantic models. It provides the necessary time-based foundation for your agg_time_dimension, ensuring time-based dimensions and metrics aggregate correctly and adhere to best-practice validation workflows.

Yes, you can define custom business metrics in dbt semantic models. Within the semantic_model YAML block, you specify metric definitions alongside your primary entities and dimensions, allowing MetricFlow to consistently calculate and expose them.