Quick Start

Get droughty running in five minutes.

Step 1 — Install

pip install droughty

Requires Python >=3.9, <3.12.4. See Installation for virtual environment setup.

Step 2 — Create your profile

Create ~/.droughty/profile.yaml with your warehouse credentials.

BigQuery (using Application Default Credentials — recommended):

First authenticate with Google Cloud:

gcloud auth application-default login

Then create the profile — no key file needed:

my_profile:
  warehouse_name: big_query
  project_name: my-gcp-project
  schema_name: analytics
  test_schemas:
    - my_staging_schema
    - my_integration_schema
    - my_analytics_schema
  dbml_schemas:
    - my_staging_schema
    - my_integration_schema
    - my_analytics_schema

Snowflake:

my_profile:
  warehouse_name: snowflake
  account: myaccount.us-east-1
  user: myuser
  password: mypassword
  warehouse: compute_wh
  database: my_database
  schema: analytics
  role: my_role
  test_schemas:
    - staging
    - integration
    - analytics
  dbml_schemas:
    - staging
    - integration
    - analytics

See Authentication for all authentication options including service account keys and OAuth.

Step 3 — Create your project file

Create droughty_project.yaml at the root of your git repository:

profile: my_profile

lookml_path: lookml/
lookml_base_filename: base
lookml_explore_filename: explores
lookml_measures_filename: measures

dbt_path: models/
dbt_tests_filename: schema

dbml_path: db_docs/
dbml_filenames:
  - analytics_layer

cube_path: schema/

Note

The profile value must exactly match a key in your ~/.droughty/profile.yaml.

Step 4 — Run a command

# Generate dbt tests for all your schemas
droughty dbt

# Generate LookML views, explores and measures
droughty lookml

# Generate a DBML ERD
droughty dbml

Droughty writes files to paths relative to the root of your git repository.

What to expect

After running droughty dbt, you’ll find a new (or updated) models/schema.yml containing:

  • Every table in your test_schemas as a model

  • Column definitions with data types

  • unique and not_null tests on every primary key column

  • not_null tests on all other columns

  • expression_is_true: valid_from <= valid_to where those columns exist

After running droughty lookml, you’ll find LookML .lkml files in lookml/ with:

  • A view for every table in your analytics schema

  • Dimensions mapped to the correct LookML types

  • PKs and FKs automatically hidden

  • Column descriptions pulled from the warehouse where available

Naming conventions

Droughty identifies keys by column name suffix:

  • Columns ending in _pk → treated as primary keys (hidden in LookML, tested for uniqueness in dbt)

  • Columns ending in _fk → treated as foreign keys (hidden in LookML, used for DBML relationships)

Next steps