Load Hudu data to DuckDB

Hudu is a documentation platform for IT services that provides a REST API for automating IT documentation management. Everything needed to build a working Hudu → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Hudu to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Hudu to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Hudu API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Hudu API at a glance

These values come from the Hudu API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Hudu API?

All API requests must include an 'x-api-key' header containing your unique API key, which can be generated in the Hudu admin area under API Keys.

1. Get your credentials

1. Log in to your Hudu instance as an administrator.\n2. Navigate to Admin > API Keys (or sometimes Account Administration > API).\n3. Click + New API Key.\n4. Provide a descriptive name and configure the required permission scopes (e.g., access to passwords, export, delete) based on your integration's needs.\n5. Save the key and copy it immediately; it will be obfuscated or hidden after you leave the page.

2. Add them to .dlt/secrets.toml

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[sources.hudu_source]
api_key = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Hudu data can I load into DuckDB?

These are the Hudu endpoints dlt can load into DuckDB:

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How do I load only new Hudu records?

The Hudu API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "companies", "endpoint": {
    "path": "api/v1/companies",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Hudu pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading companies and assets from the Hudu API into DuckDB:

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

@dlt.source
def hudu_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://[YOUR_DOMAIN]/api/v1",
            "auth": {"type": "api_key", "api_key": api_key, "name": "x-api-key", "location": "header"},
        },
        "resources": [
            {"name": "companies", "endpoint": {"path": "api/v1/companies"}},
            {"name": "assets", "endpoint": {"path": "api/v1/assets"}}
        ],
    }
    yield from rest_api_resources(config)


def load_hudu_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hudu_pipeline",
        destination="duckdb",
        dataset_name="hudu_data",
    )
    load_info = pipeline.run(hudu_source())
    print(load_info)


if __name__ == "__main__":
    load_hudu_to_duckdb()

Run it with python hudu_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Hudu data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("hudu_pipeline").dataset()
df = data.companies.df()
print(df.head())

SQL:

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SELECT * FROM hudu_data.companies LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Hudu to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Hudu loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Hudu data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack