Metabase on a dbt project

Metabase can serve as a semantic layer on top of dbt-built warehouse marts by syncing the warehouse schema, exposing finished marts to users, and defining reusable metrics and table measures that power dashboards and Metabot’s natural-language analysis. dbt retains responsibility for row-level transformations, joins, tests, and marts, while Metabase manages aggregations, metric exploration, presentation metadata, and self-service reporting; schema syncs can run hourly or be triggered after dbt jobs, and table visibility can distinguish staging, intermediate, and final layers. dbt model and column descriptions can be copied into Metabase through its API or the community-maintained dbt-metabase package, while Pro and Enterprise users can version Library content, metrics, dashboards, and other assets through Git-based Remote Sync. Metabot can query marts, metrics, measures, segments, and curated content, with glossary definitions and collection restrictions improving consistency, and it is available through Metabase, Slack, and MCP-compatible AI clients. The text contrasts this approach with Lightdash, which defines metrics directly in dbt YAML, the dbt Semantic Layer and MetricFlow, which can share metrics across multiple tools, and Cube, which targets headless semantic-layer implementations, while positioning Metabase for organizations seeking broad self-service exploration and reporting. Core metrics, measures, glossary support, API keys, MCP, and use of a customer-provided AI provider are available in the free self-hosted edition, whereas Library, verification, Remote Sync, serialization, and advanced security require Pro or Enterprise plans.

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