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Intro to the dbt Semantic Layer

Flying cars, hoverboards, and true self-service analytics: this is the future we were promised. The first two might still be a few years out, but real self-service analytics is here today. With dbt Cloud's Semantic Layer, you can resolve the tension between accuracy and flexibility that has hampered analytics tools for years, empowering everybody in your organization to explore a shared reality of metrics. Best of all for analytics engineers, building with these new tools will significantly DRY up and simplify your codebase. As you'll see, the deep interaction between your dbt models and the Semantic Layer make your dbt project the ideal place to craft your metrics.

Learning goals

  • ❓ Understand the purpose and capabilities of the dbt Semantic Layer, particularly MetricFlow as the engine that powers it.
  • 🧱 Familiarity with the core components of MetricFlow — semantic models and metrics — and how they work together.
  • 🔁 Know how to refactor dbt models for the Semantic Layer.
  • 🏅 Aware of best practices to take maximum advantage of the Semantic Layer.

Guide structure overview

  1. Getting setup in your dbt project.
  2. Building a semantic model and its fundamental parts: entities, dimensions, and measures.
  3. Building a metric.
  4. Defining advanced metrics: ratio and derived types.
  5. File and folder structure: establishing a system for naming things.
  6. Refactoring marts and roll-ups for the Semantic Layer.
  7. Review best practices.

If you're ready to ship your users more power and flexibility with less code, let's dive in!


MetricFlow is the engine for defining metrics in dbt and one of the key components of the dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics.

To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a dbt Cloud Team or Enterprise account. Refer to dbt Semantic Layer FAQs for more information.