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Unit test dbt models with standard python tooling

Project description

Pytest Plugin for DBT

This pytest plugin for dbt allows you to write unit tests for your dbt models. It makes testing your dbt models straightforward and efficient.


Install the plugin using pip:

pip install pytest-dbt


Follow these steps to use the plugin:

  1. Create a dbt model as you normally would.
  2. Run dbt compile.
  3. Update or create a pytest.ini file, and set the dbt_manifest_file_path to point to the correct manifest file. For example:
dbt_manifest_file_path = target/manifest.json
  1. Create tests using pytest. For instance, if we have a model order_size and we want to confirm it has three columns, we can create a test file and add the following test:
customer_no_order = [
    {'order_id': 1 , "order_size": 'small'},
    {'order_id': 2 , "order_size": 'large'},
    {'order_id': 3 , "order_size": 'medium'},
    {'order_id': 4 , "order_size": 'large'},
    {'order_id': 5 , "order_size": 'large'},

tables = {
    'orders': orders,

def test_customers_no_order():
    result = run_model('order_size', tables)
    assert assert_frame_equal(result, pd.DataFrame(customer_no_order)) == None
  1. Run the tests using the command pytest:

Why Not Use DBT Test?

While DBT Test is excellent for integration tests, it is less suited for unit testing.

Why Pytest?

pytest is a mature testing framework with a wide range of plugins and strong community support.

Future Improvements

Here are the planned enhancements for the plugin:

  • Add support for User-Defined Functions (UDFs).
  • Implement a cyclic redundancy check (CRC) to monitor changes in the model.
  • Incorporate a coverage report for SQL files.
  • Add a command line option for passing in the test configuration. This will be especially helpful for Continuous Integration (CI) scenarios.
  • Address issues related to missing files in test_dbt_manifest_file_path = dbt_manifest.json.
  • Resolve the AssertionError: File does not exist at path: dbt_manifest.json error.

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