Acquia Site Factory Python API Docs | dltHub

Acquia Site Factory API is a REST interface that lets you create and manage websites on the Acquia platform, trigger jobs, create backups, and clear caches. The REST API base URL is https://{site_URL}/api/v1 and All requests require HTTP Basic authentication with a Site Factory username and API key..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Acquia Site Factory data in under 10 minutes.

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What data can I load from Acquia Site Factory?

Here are some of the endpoints you can load from Acquia Site Factory:

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How do I authenticate with the Acquia Site Factory API?

Authentication is performed via HTTP Basic Auth. Include the Site Factory username as the user name and the API key as the password, e.g., curl -u username:api_key.

1. Get your credentials

1. Log in to the Site Factory Management Console with a role that can manage API keys (e.g., platform admin or developer).
2. Click your user name in the top‑right menu.
3. Open the "API key" tab.
4. Copy the displayed API key.
5. Use this key together with your Site Factory username for HTTP Basic authentication.

2. Add them to .dlt/secrets.toml

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[sources.acquia_site_factory_source]
username = "your_username"
api_key = "your_api_key"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Acquia Site Factory API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python acquia_site_factory_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline acquia_site_factory_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset acquia_site_factory_data
The duckdb destination used duckdb:/acquia_site_factory.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline acquia_site_factory_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads sites and groups from the Acquia Site Factory API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def acquia_site_factory_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{site_URL}/api/v1",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "sites", "endpoint": {"path": "sites"}},
            {"name": "groups", "endpoint": {"path": "groups"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="acquia_site_factory_pipeline",
        destination="duckdb",
        dataset_name="acquia_site_factory_data",
    )
    load_info = pipeline.run(acquia_site_factory_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("acquia_site_factory_pipeline").dataset()
sessions_df = data.sites.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM acquia_site_factory_data.sites LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("acquia_site_factory_pipeline").dataset()
data.sites.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Acquia Site Factory data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive a 401 Unauthorized response, verify that you are using the correct Site Factory username and API key and that your account has a role (e.g., platform admin or developer) that permits API access.

Insufficient permissions

Users without the required role will not see the full API documentation and may receive 403 Forbidden errors when calling endpoints.

SSL certificate warnings

When accessing staging environments, the API may use a self‑signed certificate. Use the -k flag with curl to ignore certificate validation, or configure your HTTP client to trust the certificate.

Pagination

The current documentation does not describe pagination parameters. If a response appears truncated, consult the API reference for possible page or limit query parameters.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack