Data Europa Python API Docs | dltHub

data.europa.eu provides various REST APIs to manage, search, and access metadata for European open datasets following DCAT-AP standards. The REST API base URL is https://data.europa.eu/api/hub/ and Restricted endpoints require a JWT Bearer token obtained through an authentication middleware..

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 Data Europa data in under 10 minutes.

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What data can I load from Data Europa?

Here are some of the endpoints you can load from Data Europa:

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How do I authenticate with the Data Europa API?

Requests to restricted API endpoints require a Bearer token in the 'Authorization' header, which is obtained by exchanging client credentials via the 'https://data.europa.eu/auth/middleware/login/service' endpoint.

1. Get your credentials

Data.europa.eu uses a middleware for access control to restricted API endpoints (such as Registry and Store). To obtain an access token, you must have a service account associated with a catalogue. You obtain a JWT Bearer token by sending a POST request to the authentication endpoint using your service account's client ID and client secret: \n\ncurl --location 'https://data.europa.eu/auth/middleware/login/service' \\n--header 'Accept: application/json' \\n--header 'Content-Type: application/json' \\n--data '{\n "client_id": "YOUR_CLIENT_ID",\n "client_secret": "YOUR_CLIENT_SECRET"\n}'\n\nThe response will contain an "access_token" field, which you then pass in the Authorization header of subsequent requests as "Authorization: Bearer <ACCESS_TOKEN>". There is no public user dashboard for self-service API key generation; credentials for service accounts are typically provisioned through administrative contact with the data portal operators.

2. Add them to .dlt/secrets.toml

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[sources.data_europa_source]
credentials = "REPLACE_ME"

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 harness:

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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 Data Europa 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 data_europa_pipeline.py

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

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Pipeline data_europa_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset data_europa_data
The duckdb destination used duckdb:/data_europa.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 datasets and data from the Data Europa 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 data_europa_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data.europa.eu/api/hub/",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "api/hub/search/datasets"}},
            {"name": "catalogues", "endpoint": {"path": "api/hub/search/catalogs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="data_europa_pipeline",
        destination="duckdb",
        dataset_name="data_europa_data",
    )
    load_info = pipeline.run(data_europa_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("data_europa_pipeline").dataset()
sessions_df = data.datasets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM data_europa_data.datasets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("data_europa_pipeline").dataset()
data.datasets.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 Data Europa 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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Next steps

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

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — 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-platform

• 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