10Duke Python API Docs | dltHub

10Duke is an enterprise identity, entitlement and licensing platform exposing REST and Graph APIs to manage organisations, users, devices, groups and entitlements. The REST API base URL is {deployment_base_url}/api/ and all requests require an authorization header (bearer token or deployment-specific API token) per the API authorization docs.

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 add "dlt[hub]" and start loading 10Duke data in under 10 minutes.

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What data can I load from 10Duke?

Here are some of the endpoints you can load from 10Duke:

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How do I authenticate with the 10Duke API?

10Duke uses token-based authorization for its APIs. Include Authorization: Bearer (or other deployment-issued token) in request headers; some APIs (e.g. invitation links) use URL tokens where specified.

1. Get your credentials

1. Log into your 10Duke SysAdmin / admin portal for your deployment. 2) Navigate to the API / Integrations or Service Accounts section. 3) Create a service account or API client and assign necessary roles/permissions (identity/entitlement access). 4) Copy the generated bearer token or client credentials. 5) If using OAuth/OpenID Connect, register a client and obtain client_id/client_secret and perform token exchange per Authentication API docs.

2. Add them to .dlt/secrets.toml

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[sources.api_10duke_graph_api_source]
api_token = "your_api_token_here"

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 init
uv add "dlt[hub]"

1. Install the dlt AI Workbench:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 10Duke 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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uv run python api_10duke_graph_api_pipeline.py

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

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Pipeline api_10duke_graph_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset api_10duke_graph_api_data
The duckdb destination used duckdb:/api_10duke_graph_api.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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uv run dlthub 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 users and groups from the 10Duke 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 api_10duke_graph_api_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "{deployment_base_url}/api/",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "identity/users/", "data_selector": "users"}},
            {"name": "groups", "endpoint": {"path": "identity/groups/", "data_selector": "groups"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="api_10duke_graph_api_pipeline",
        destination="duckdb",
        dataset_name="api_10duke_graph_api_data",
    )
    load_info = pipeline.run(api_10duke_graph_api_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("api_10duke_graph_api_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM api_10duke_graph_api_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("api_10duke_graph_api_pipeline").dataset()
data.users.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 10Duke 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 401/403 responses, verify the Authorization header contains a valid Bearer token and that the service account has the required roles. Invitation endpoints may require token-in-URL formats as documented.

Rate limits and throttling

The documentation warns to follow deployment-specific rate limits. On 429 responses, implement exponential backoff and retry according to server Retry-After header.

Graph API deprecation and differences

The Graph API endpoint /graph/ is deprecated; for production integrations prefer the REST Identity and Entitlement APIs. Graph responses use operation-specific selectors (the JSON key containing results varies by graph selector), so inspect the response body when building selectors.

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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• 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