select statements, and dbt handles the dependencies between them, builds the resulting tables and views, and tests them.
Lightdash reads a connected dbt project and builds your semantic layer from it. Your dbt models define what the tables are; the Lightdash metadata in their YAML defines what they mean — which columns are dimensions, which aggregations are metrics, how models join.
dbt is not required. If you don’t use it, define the same semantic layer in Lightdash YAML instead. This area covers the dbt integration itself: connecting a project, keeping it in sync, moving definitions between Lightdash and your repository, and what changes when you upgrade dbt.
For how to shape models so they explore well, see modeling strategies.dbt
dbt
Connect a dbt project to Lightdash, keep it in sync, and use metrics defined in dbt
dbt is the industry-standard tool for transforming data in your warehouse. You write modular transformations as plain SQL
dbt Projects
Every way to sync your dbt project into Lightdash, and how to connect multiple dbt sources
dbt write-back
Develop models and metrics in Lightdash and open a pull request against your dbt project
dbt MetricFlow metrics
Connect Lightdash to your dbt MetricFlow semantic layer metrics
Upgrading dbt
What changes in your Lightdash setup when you move between dbt versions