Configure a development environment¶
This guide configures the checked-in bricksgdpr dbt profile to use Databricks OAuth and installs
the repository's pinned Python dependencies.
Prerequisites¶
You need:
- Databricks CLI 1.7.0 or newer;
uv;- access to a Databricks workspace; and
- the repository checked out locally.
Install jq as well if you will provision or validate persona identities.
Create the local environment file¶
From the repository root:
Fill every blank workspace/account value needed by the workflows you intend to run. The example contains no real workspace host, warehouse ID, account ID, group ID, or operator email.
Warning
Do not overwrite an existing .env until you have reviewed its target. The file is ignored by
Git and can contain machine-specific identifiers, but it must never contain the
pseudonymization pepper or a Databricks token.
Review these dbt connection values in .env:
DATABRICKS_HOST
DATABRICKS_HTTP_PATH
DBT_CONTROL_CATALOG
DBT_CONTROL_SCHEMA
DBT_PROJECT_CATALOG
DBT_SCHEMA_PREFIX
The checked-in profiles.yml reads them and uses auth_type: oauth.
Authenticate the CLI profile¶
Confirm that the profile resolves successfully:
The response should report a successful authentication for the expected workspace host.
Load the variables¶
This exports the values for both dbt and the repository scripts in the current shell.
Install and parse¶
uv sync --all-extras installs the pinned dbt-core and dbt-databricks versions plus ruff
and ty. A successful parse confirms that dbt can load the project configuration and write its
local artifacts.
Note
dbt parse does not create the Unity Catalog catalog, secret, groups, or model relations.
Complete Deploy the demo for the first time for a new
workspace.
Consult Project configuration for every environment variable and default.