dbt-bouncer 0.14.3

Configure and enforce conventions for your dbt project.

dbt-bouncer

Configure and enforce conventions for your dbt project.

How to use

1. Generate a manifest.json by running dbt parse.
2. Create a dbt-bouncer.yml config file, details here.
3. Run dbt-bouncer to validate that your conventions are being maintained. You can use GitHub Actions, Docker, a .pex file or python to run dbt-bouncer.

GitHub Actions

steps:
    ...

    - uses: godatadriven/dbt-bouncer@v0
      with:
        config-file: ./<PATH_TO_CONFIG_FILE>
        send-pr-comment: true # optional, defaults to true

    ...

Docker

Don't use GitHub Actions? You can still use dbt-bouncer via Docker:

docker run --rm \
    --volume "$PWD":/app \
    ghcr.io/godatadriven/dbt-bouncer:vX.X.X \
    --config-file /app/<PATH_TO_CONFIG_FILE>

Pex

You can also run the .pex (Python EXecutable) artifact directly once you have a python executable installed:

wget https://github.com/godatadriven/dbt-bouncer/releases/download/vX.X.X/dbt-bouncer.pex -O dbt-bouncer.pex

dbt-bouncer.pex --config-file $PWD/<PATH_TO_CONFIG_FILE>

Python

Install from pypi.org:

pip install dbt-bouncer

Run:

dbt-bouncer.pex --config-file $PWD/<PATH_TO_CONFIG_FILE>

Config file

dbt-bouncer requires a config file to be provided. This file configures what checks are run. Here is an example config file:

dbt-artifacts-dir: target # [Optional] Directory where the dbt artifacts exists, generally the `target` directory inside a dbt project. Defaults to `./target`.

manifest_checks:
  - name: check_macro_name_matches_file_name
  - name: check_model_names
    include: ^staging
    model_name_pattern: ^stg_

Checks

:bulb: Click on a check name to see more details.

Macros

• check_macro_arguments_description_populated: Macro arguments must have a populated description.
• check_macro_description_populated: Macros must have a populated description.
• check_macro_name_matches_file_name: Macros names must be the same as the file they are contained in.

Metadata

• check_project_name: Enforce that the name of the dbt project matches a supplied regex.

Miscellaneous

• check_top_level_directories: Only specified top-level directories are allowed to contain models.

Models

• check_model_description_populated: Models must have a populated description.
• check_model_names: Models must have a name that matches the supplied regex.

Sources

• check_source_has_meta_keys: The meta config for sources must have the specified keys.

Development

To setup your development environment, fork this repository and run:

poetry shell
poetry install
pre-commit install

Set required environment variables by copying .env.example to .env and updating the values.

All tests can be run via:

make build-artifacts # Rebuilds dbt artifacts used by pytest
make test