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Three steps in sequence: install the CLI tool, prepare your Tables, create a project
In Lightdash, everything you need for BI is written as code in your dbt project. You use dbt to transform all of the data from your data warehouse, then you use Lightdash to explore it. But, before you hook up your dbt project to Lightdash, we need to make sure we have Tables to explore. In this setup guide, we’ll walk you through the steps of installing + using the Lightdash CLI and creating the first Tables in your project.
New to dbt? If you haven’t used dbt before, follow dbt’s getting started guide before proceeding with setting up Lightdash.

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What are Tables?

Tables are the starting point for any data exploration in Lightdash — they’re the data you can query. Lightdash stays in sync with your dbt project, so Tables come from the dbt models defined in your project’s YAML files. If a dbt model has a YAML definition, it shows up in Lightdash as a Table.
Not sure what a YAML file is? Check out dbt’s docs on model properties to learn about adding YAML files for your dbt models.

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Choose which models become Tables

lightdash dbt run documents every column in your models as a dimension and generates a Table for each one. It supports dbt’s model selection syntax, so you can generate Tables for your whole project at once or narrow to a subset — for example, everything tagged lightdash:
Now that you know what Tables are, let’s install the CLI and create your first project.

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Step 1: Install the Lightdash CLI tool

The Lightdash CLI is the recommended way to develop your dbt + Lightdash project. It makes development faster and easier, as well as giving you options for building more powerful automation to manage your Lightdash instance. Install it with Homebrew (macOS, Linux, or Windows via WSL):
Don’t have Homebrew, or need another install method (direct binary download, npm)? See the full CLI installation guide.

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Step 2: Login to the CLI

We’ve automatically generated a personal access token that you can use to login to the CLI, so you just need to copy-paste that command in your Terminal. It’s Step 2. and should look something like this:
Where {{ lightdash_domain }} is app.lightdash.cloud for Lightdash Cloud users. Or your own domain if self-hosting. If you want to login using another method, then you can check out our authentication methods here.

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Step 3: Add Tables

If you’re using dbt Cloud, you’ll need to clone your repo to your local machine before completing the next steps.
To get our dbt project Lightdash-ready, we need to define all of the columns in our dbt models that we want to explore in Lightdash. We’ve made this really easy to do using our CLI tool and the command:
This will generate Tables and dimensions for your entire dbt project. It will document all of the columns in your models and these are what we explore in Lightdash as Dimensions. For example, if we have a leads.yml file, we’d have a Table called Leads and it would have the dimensions: Lead id, User id, and sdr.
A leads model with lead_id, user_id, and sdr columns becoming a Leads table in Lightdash with Lead id, Sdr, and User id dimensions
If you’re getting an error: Error: Not a dbt project (or any of the parent directories). Missing dbt\_project.yml fileMake sure that you’re in your dbt project folder (i.e. where your dbt_project.yml file is) before you try doing lightdash dbt run
To generate Tables for only some of your models, use the dbt selection syntax shown above.

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Step 4: Create your project

Now, your dbt project is Lightdash-ready! Just run this command and follow the link to start exploring your data:

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Next steps: Update your project connection settings

We create your first project using your local dbt profiles.yml file. So, you might want to update your connection settings to:
  1. Set up a service account for Lightdash to run on (instead of using your personal connection details)
  2. Connect Lightdash to your version controlled dbt project so changes are automatically synced.
Check out details on updating your connection settings here.
Project connection settings with the warehouse authentication type set to a service account and the dbt connection set to GitHub