Nacnud Python API Docs | dltHub

Nacnud is an identity and access management platform that exposes a REST API for authentication and access‑control flows. The REST API base URL is `` and .

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 Nacnud data in under 10 minutes.

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What data can I load from Nacnud?

Here are some of the endpoints you can load from Nacnud:

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How do I authenticate with the Nacnud API?

Not publicly documented; the vendor's API manual is password‑protected and access requires contacting the provider.

1. Get your credentials

Contact Nacnud (or the documentation owner) to request access to the password‑protected API manual and obtain developer credentials. The liblynx article states you must 'get in touch' to view the manual.

2. Add them to .dlt/secrets.toml

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[sources.nacnud_source]

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 Nacnud 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 nacnud_pipeline.py

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

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Pipeline nacnud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nacnud_data
The duckdb destination used duckdb:/nacnud.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 nacnud_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 from the Nacnud 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 nacnud_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nacnud_pipeline",
        destination="duckdb",
        dataset_name="nacnud_data",
    )
    load_info = pipeline.run(nacnud_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("nacnud_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nacnud_data. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nacnud_pipeline").dataset()
data..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 Nacnud 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

Access to API Documentation

The Nacnud API documentation is not publicly available and is protected by a password. If you encounter authentication failures or cannot locate endpoints, you need to contact Nacnud (or the documentation owner) to request access to the manual and obtain the necessary credentials.

Generic Recommendations

• Verify that you have received the correct base URL and authentication details from the provider.
• Ensure that any required API keys or tokens are included in the request headers as specified in the manual.
• If you receive HTTP 401/403 responses, it most likely indicates missing or incorrect credentials.
• For any rate‑limit or pagination behavior, refer to the official manual once it is obtained.

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