Load Opendatasoft data to DuckDB

Opendatasoft (now Huwise) is an open data platform providing RESTful APIs for searching, browsing, and querying datasets on data portals. Everything needed to build a working Opendatasoft → 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 Opendatasoft 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 Opendatasoft 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 Opendatasoft 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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Opendatasoft API at a glance

These values come from the Opendatasoft API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Opendatasoft API?

Authentication can be performed using an API key provided in the Authorization header as 'Authorization: Apikey <API_KEY>' or as a query parameter 'apikey=<API_KEY>'. OAuth2 is also supported, utilizing Bearer tokens in the Authorization header.

1. Get your credentials

To obtain your Opendatasoft (Huwise) API credentials, follow these steps: 1. Log in to your Opendatasoft portal. 2. Click on your username in the top-right corner to open the user menu. 3. Select 'Your account' to navigate to your profile page. 4. Go to the 'API keys' tab. 5. Click the '+ Generate a new API key' button. 6. Provide a descriptive label for the key, select the appropriate permissions (keep these as restrictive as possible), and click create/generate. 7. Copy the generated API key immediately as it will be used for your requests.

2. Add them to .dlt/secrets.toml

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[sources.opendatasoft_source]
api_key = "your_generated_api_key_here"

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 Opendatasoft data can I load into DuckDB?

These are the Opendatasoft endpoints dlt can load into DuckDB:

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How do I load only new Opendatasoft records?

Opendatasoft exposes offset on catalog/datasets/{dataset_id}/records, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "records", "endpoint": {
    "path": "catalog/datasets/{dataset_id}/records",
    "data_selector": "results",
    "incremental": {"cursor_path": "offset", "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 Opendatasoft pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading catalog/datasets and catalog/datasets/{dataset_id}/records from the Opendatasoft API into DuckDB:

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

@dlt.source
def opendatasoft_source(apikey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{domain}/api/explore/v2.1 or https://{domain}/api/odata",
            "auth": {"type": "api_key", "api_key": apikey, "name": "Authorization", "location": "header"},
        },
        "resources": [
            {"name": "records", "endpoint": {"path": "catalog/datasets/{dataset_id}/records", "data_selector": "results"}},
            {"name": "catalog", "endpoint": {"path": "catalog/datasets", "data_selector": "datasets"}}
        ],
    }
    yield from rest_api_resources(config)


def load_opendatasoft_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="opendatasoft_pipeline",
        destination="duckdb",
        dataset_name="opendatasoft_data",
    )
    load_info = pipeline.run(opendatasoft_source())
    print(load_info)


if __name__ == "__main__":
    load_opendatasoft_to_duckdb()

Run it with python opendatasoft_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 Opendatasoft 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("opendatasoft_pipeline").dataset()
df = data.records.df()
print(df.head())

SQL:

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SELECT * FROM opendatasoft_data.records LIMIT 10;

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

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How do I deploy the Opendatasoft 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 Opendatasoft 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 Opendatasoft 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