dbt-bouncer 1.2.3

Configure and enforce conventions for your dbt project.

dbt-bouncer

Configure and enforce conventions for your dbt project.

Documentation

All documentation can be found on dbt-bouncer documentation website.

TLDR

1. Install dbt-bouncer:

   pip install dbt-bouncer
   

2. Create a dbt-bouncer.yml config file:

   manifest_checks:
     - name: check_model_directories
       include: ^models
       permitted_sub_directories:
         - intermediate
         - marts
         - staging
     - name: check_model_names
       include: ^models/staging
       model_name_pattern: ^stg_
   catalog_checks:
     - name: check_columns_are_documented_in_public_models
   run_results_checks:
     - name: check_run_results_max_execution_time
       max_execution_time_seconds: 60
   

3. Run dbt-bouncer:

   $ dbt-bouncer
   
   [...]
   Running checks... |################################| 20/20
   Done. SUCCESS=19 WARN=0 ERROR=1
   Failed checks:
   | Check name               | Severity | Failure message                                                                       |
   |--------------------------|----------|---------------------------------------------------------------------------------------|
   | check_model_directories: | error    | AssertionError: `model` is located in `utilities`, this is not a valid sub-directory. |
   

Reporting bugs and contributing code

• Want to report a bug or request a feature? Let us know and open an issue.
• Want to help us build dbt-bouncer? Check out the Contributing Guide.

Code of Conduct

Everyone interacting in dbt-bouncer's codebase, issue trackers, chat rooms, and mailing lists is expected to follow the Code of Conduct.