data_pipelines_cli.cli_commands package

Subpackages

data_pipelines_cli.cli_commands.generate package

Submodules

data_pipelines_cli.cli_commands.clean module

clean() → None[source]: Delete local working directories.

data_pipelines_cli.cli_commands.compile module

compile_project(env: str, docker_tag: Optional[str] = None, docker_build: bool = False, docker_build_args: Optional[Dict[str, str]] = None) → None[source]

Create local working directories and build artifacts.

Parameters

env (str) – Name of the environment
docker_tag (Optional[str]) – Image tag of a Docker image to create
docker_build (bool) – Whether to build a Docker image
bi_build – Whether to generate a BI codes

Raises

DataPipelinesError –

replace_image_settings(image_tag: str) → None[source]

data_pipelines_cli.cli_commands.create module

data_pipelines_cli.cli_commands.deploy module

data_pipelines_cli.cli_commands.docs module

docs(env: str, port: int) → None[source]

Generate and serve dbt documentation.

Parameters

env (str) – Name of the environment
port (int) – Port to serve dbt documentation on.

data_pipelines_cli.cli_commands.init module

data_pipelines_cli.cli_commands.prepare_env module

prepare_env(env: str) → None[source]

Prepare local environment for use with dbt-related applications.

Prepare local environment for use with applications expecting a “traditional” dbt structure, such as plugins to VS Code. If in doubt, use dp run and dp test instead.

Parameters: env (str) – Name of the environment

data_pipelines_cli.cli_commands.publish module

data_pipelines_cli.cli_commands.run module

run(env: str) → None[source]

Run the project on the local machine.

Parameters: env (str) – Name of the environment

data_pipelines_cli.cli_commands.seed module

seed(env: str) → None[source]