dbt-webhook 0.3.7

A DBT plugin that allows defining HTTP hooks for DBT commands and/or models, triggering them at execution start/end, and injecting the returned data into the model's metadata.

dbt-webhook

A dbt plugin that allows defining HTTP hooks for DBT commands and/or models, triggering them at execution start/end, and injecting the returned data into the model's metadata.

How It Works

Below is a diagram describing how the plugin works and the different types of hooks:

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Getting Started

dbt-webhook is designed to work with dbt-core, allowing data infrastructure teams to extend functionality via additional Python packages.

To start using dbt-webhook, follow these three steps:

1. Install the dbt-webhook Package

Install dbt-webhook in your local development environment and in the environment running scheduled jobs (e.g., Airflow):

pip install dbt-webhook

2. Create a dbt-webhook Configuration File

Create in work dir file dbt_webhook.yml and configure hooks you want to run.

Example Configuration

command_start_hook:
  command_types:
    - "compile"
  webhook_url: "https;//domain.example.com/dbt/command/start"
  webhok_method: "POST"
  headers:
    Content-Type: "application/json"

command_end_hook:
    # Same schema as above

model_start_hook:
    # Same schema as above

model_end_hook:
    # Same schema as above

Configuration Notes:

• By default, the plugin will search for dbt_webhook.yml in the working directory:
  • In VSCode (when using the dbt Power User extension), this is the workspace directory.
  • In Airflow, the working directory may differ (e.g., the project directory).
  • If the local and scheduled work directories differ, you can create a symlink to avoid duplicating the configuration file.
• If the HTTP endpoint requires authentication, pass secrets via environment variables:

  headers:
    Authorization: "bearer {DBT_WEBHOOK_AUTH_TOKEN}"
    Content-Type: "application/json"
  env_vars:
    - "DBT_WEBHOOK_AUTH_TOKEN"

3. Create HTTP Endpoints

The format of request and response payloads is defined in dbt_webhook/webhook_model.py using Pydantic models.

Command Hooks Request/Response Schema

{
    "command_type": "run",
    "invocation_id": "<UUID generated on dbt command start>",
    "run_started_at": "<timestamp>",
    "run_finished_at": "<timestamp>",
    "success": True | False | None
    "nodes": {
        "model.project.my_first_dbt_model": {
            "unique_id": "model.project.my_first_dbt_model",
            "meta": {"field1": 'val1'},
            "node_started_at": "<timestamp>",
            "node_finished_at": "<timestamp>",
            "target_database": "<db | project>",
            "target_schema": "<dataset | schema>",
            "target_table_name": "my_first_dbt_model",
            "success": True | False | None,
        },
        "<some other model unique id>": {...},
        ...
    },
}

Node Hooks Request/Response Schema

For node hooks, the schema is similar, but instead of a nodes dictionary, there is a single node object:

{
    "command_type": "run",
    "invocation_id": "<UUID generated on dbt command start>",
    "run_started_at": "<timestamp>",
    "run_finished_at": "<timestamp>",
    "success": True | False | None
    "node": {
        "unique_id": "model.project.my_first_dbt_model",
        "meta": {"field1": 'val1'},
        "node_started_at": "<timestamp>",
        "node_finished_at": "<timestamp>",
        "target_database": "<db | project>",
        "target_schema": "<dataset | schema>",
        "target_table_name": "my_first_dbt_model",
        "success": True | False | None,
    },
}

Contributing

Contributions are welcome! Feel free to submit issues or open pull requests on GitHub.