Configuration

Setting up droughty to run is pretty easy. It depends on two files, a droughty_project.yaml file within the root of your repo and a profile.yaml file within a .droughty/ dir within your user dir

droughty_project.yaml set-up

To differentiate between multiple warehouse targets within the profiles.yaml file, droughty uses a droughty_project.yaml to specify a project specific target. Find an droughty_project.yaml file below:

profile: example_project

dimensional_inference: enabled

field_description_path: warehouse_docs
field_description_file_name: field_descriptions.md

openai_field_descriptions_path: warehouse_docs
openai_field_descriptions_filename: openai_field_descriptions

test_schemas:
    - lewis_analytics_dev_staging
    - lewis_analytics_dev_integration
    - lewis_analytics_dev


test_overwrite:
    models:
      wh_marketing__web_event_items_fact:
            web_event_item_pk:
                - not_null
                - dbt_utils.at_least_one
                - unique
            web_event_parameter_float_value:
                - dbt_utils.at_least_one

test_ignore:
    models:
      - base_backend__web_events
      - base_ga4__web_events


dbml_schemas:
    - lewis_analytics_dev_staging
    - lewis_analytics_dev_integration
    - lewis_analytics_dev

dbml_filenames:
    - test_10
    - test_11
    - test_12

explores:
  parent_table:
    - example_parent
  dimensions:
    - example_dim
  facts:
    - example_fact

lookml_pop:
    views:
        example_1:
                - example_2
        example_3:
                - example_4
                - example_5

lookml_base_filename: example__1
lookml_explore_filename: example__2
lookml_measures_filename: example__3
cube_base_filename: example__4
cube_integration_filename: example__5
cube_measures_filename: example__6
dbt_tests_filename: example__8

entity_resolution:
  read_schema:
    example_1
  write_schema:
    example_1
  read_table_names:
    example_1:
      - example_3
      - example_4
    example_2:
      - example_5
      - example_6
  write_column_names:
    - example_7
    - example_8
  write_table_name:
    - example_9

Create this file in the root of your git repo (unless you are specifying the path through the –project-dir argument)

Optional variables

Overwriting and ignoring model tests

Using the test_overwrite and test_ignore project parameters, you can overwrite tests or leave them blank using the test_overwrite parameter or ignore all model tests using the test_ignore parameter

Defining relative file outputs

Just add these variables to your droughty_project.yaml and it will write to the path name starting from the root of your git repo:

dbt_path: example_path
dbml_path: example_path
lookml_path: example_path
cube_path: example_path

It’s important that the profile name with the droughty_project.yaml aligns with the paired entry within your profile.yaml.

---

profile.yaml set-up

A profile.yaml file is used to pass warehouse permissions to droughty, such as warehouse key files, project, schema names and other permissions.

This file should be created in a .droughty dir, such as:

/Users/titus_groan/.droughty/profile.yaml

Below is an example of what the profile should contain

profile example:

droughty_demo:

  host:

  key_file: /Users/droughty_user/[key_file]

  password:

  port:

  project_name: example-project

  schema_name: analytics_qa

  user:

  warehouse_name: big_query

  openai_secret: sk-wdfnwfw40t493t304t9340t94wet0et90edf (example)

---

warehouse_name options

At the moment, only ‘big_query’ and ‘snowflake’ are supported

Configuration Considerations

droughty has been developed to work with dbt, db docs and looker. However, it only really depends accessing the information schema within a supported warehouse.

When using droughty it’s assumed that the warehouse structure it points towards has at least three data sets, staging, integration and a analytics layer. Look at the usage page for further information.