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CLI for data platform

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CLI for data platform


Read the full documentation at


Use the package manager pip to install dp (data-pipelines-cli):

pip install data-pipelines-cli[bigquery,docker,datahub,gcs]


First, create a repository with a global configuration file that you or your organization will be using. The repository should contain dp.yml.tmpl file looking similar to this:

_templates_suffix: ".tmpl"
    autoescape: false
    block_end_string: "%]"
    block_start_string: "[%"
    comment_end_string: "#]"
    comment_start_string: "[#"
    keep_trailing_newline: true
    variable_end_string: "]]"
    variable_start_string: "[["

    template_name: my-first-template

  username: [[ YOUR_USERNAME ]]

Thanks to the copier, you can leverage tmpl template syntax to create easily modifiable configuration templates. Just create a copier.yml file next to the dp.yml.tmpl one and configure the template questions (read more at copier documentation).

Then, run dp init <CONFIG_REPOSITORY_URL> to initialize dp. You can also drop <CONFIG_REPOSITORY_URL> argument, dp will get initialized with an empty config.

Project creation

You can use dp create <NEW_PROJECT_PATH> to choose one of the templates added before and create the project in the <NEW_PROJECT_PATH> directory. You can also use dp create <NEW_PROJECT_PATH> <LINK_TO_TEMPLATE_REPOSITORY> to point directly to a template repository. If <LINK_TO_TEMPLATE_REPOSITORY> proves to be the name of the template defined in dp's config file, dp create will choose the template by the name instead of trying to download the repository.

dp template-list lists all added templates.

Project update

To update your pipeline project use dp update <PIPELINE_PROJECT-PATH>. It will sync your existing project with updated template version selected by --vcs-ref option (default HEAD).

Project deployment

dp deploy will sync with your bucket provider. The provider will be chosen automatically based on the remote URL. Usually, it is worth pointing dp deploy to JSON or YAML file with provider-specific data like access tokens or project names. E.g., to connect with Google Cloud Storage, one should run:

echo '{"token": "<PATH_TO_YOUR_TOKEN>", "project_name": "<YOUR_PROJECT_NAME>"}' > gs_args.json
dp deploy --dags-path "gs://<YOUR_GS_PATH>" --blob-args gs_args.json

However, in some cases you do not need to do so, e.g. when using gcloud with properly set local credentials. In such case, you can try to run just the dp deploy --dags-path "gs://<YOUR_GS_PATH>" command. Please refer to documentation for more information.

When finished, call dp clean to remove compilation related directories.


You can put a dictionary of variables to be passed to dbt in your config/<ENV>/dbt.yml file, following the convention presented in the guide at the dbt site. E.g., if one of the fields of config/<SNOWFLAKE_ENV>/snowflake.yml looks like this:

schema: "{{ var('snowflake_schema') }}"

you should put the following in your config/<SNOWFLAKE_ENV>/dbt.yml file:

  snowflake_schema: EXAMPLE_SCHEMA

and then run your dp run --env <SNOWFLAKE_ENV> (or any similar command).


Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

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