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Connecting to Fabric

In this guide, we connect a dbt-polars project to a Microsoft Fabric Lakehouse. Polars runs on your own machine, so no Spark session or SQL endpoint is needed. If you don't have a project yet, the quick start creates one.

Prerequisites​

  • A Fabric workspace on a Fabric capacity, such as an F or P SKU or a Fabric trial. The capacity must be running, because OneLake is unavailable while it's paused.
  • The Contributor role, or higher, on the workspace.
  • The Azure CLI.

Creating a Lakehouse​

dbt-polars writes its tables to a Lakehouse with schemas enabled. In your Fabric workspace:

  1. Select New item, then Lakehouse.
  2. Enter a name, for example my_lakehouse.
  3. Keep Lakehouse schemas enabled, which is the default for new Lakehouses, and select Create.

Configuring the profile​

info

For this tutorial, we authenticate to Fabric with our current user and save tables in Delta format. See the parameter reference for all configuration options.

Install the Fabric catalog:

pip install 'dbt-polars[fabric]'

dbt reads its connections from profiles.yml, by default in the ~/.dbt folder. Add a profile for your project, named after the profile setting in dbt_project.yml:

my_project:
  target: fabric
  outputs:
    fabric:
      type: polars
      schema: analytics
      catalog_type: fabric
      workspace: my_workspace
      lakehouse: my_lakehouse

Replace the workspace and lakehouse with your own. Then log in to Azure with the Azure CLI:

az login

Running the project​

Run the project:

dbt run

dbt-polars creates the analytics schema in the Lakehouse and writes your models to it as Delta tables. Open the Lakehouse in Fabric to find them under Tables.

Did you notice?

We did not use any Spark or SQL compute. Polars ran the models on your own machine and wrote the tables to OneLake, where Fabric discovers them automatically.

Read more​