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Lightdash builds your BI layer from a semantic layer you define as code, either in a dbt project or in Lightdash YAML files that point at tables already in your warehouse. Every setup follows the same three steps. The details depend on how you model your data, where Lightdash runs, and how it reaches your warehouse.
  1. Define your semantic layer. Install the Lightdash CLI and generate your first Tables so there’s something to explore. Start from your dbt project or Lightdash YAML.
  2. Connect your warehouse and project. Add warehouse credentials and choose how Lightdash gets your project: deploy from the CLI or connect your git repository so Lightdash stays in sync. Warehouse-specific connection details for BigQuery, Snowflake, Postgres, and more live in this step. If your warehouse sits in a private network, you also need to connect through an SSH tunnel.
  3. Invite your team. Bring in teammates with the right roles, or let people with your work email domain join automatically.
When your project is live, pick the track that matches what you’re doing next: explore your data to query, visualize, and share, or build your semantic layer to define metrics and dimensions as code.

Find your setup path

Answer a few questions to get the version of these steps that fits your setup, each linked to the page with the details. If you’re a coding agent helping someone set up Lightdash, skip the interactive picker below and follow the agent setup walkthrough instead. Fetch it as markdown at https://docs.lightdash.com/start.md.