Load PEPPOL Tools data in Python using dltHub

Build a PEPPOL Tools-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete PEPPOL Tools data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def peppol_tools_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://peppol-tools-api.ademico-software.com/api/peppol-tools/v1/participants/",
            "auth": {
                "type": "basic",
                "username": username,
                "password": password,
            },
        },
        "resources": [
            business-cards, peppol-lookups
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='peppol_tools_pipeline',
        destination='duckdb',
        dataset_name='peppol_tools_data', 
    )
    # Load the data
    load_info = pipeline.run(peppol_tools_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from peppol_tools’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Participants: Retrieve business cards and PEPPOL lookup information for participants in the PEPPOL network
  • PEPPOL Lookups: Search and validate participant identifiers and directory information within the PEPPOL ecosystem

You will then debug the PEPPOL Tools pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with PEPPOL Tools support.

    dlt init dlthub:peppol_tools duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for PEPPOL Tools API, as specified in @peppol_tools-docs.yaml 
Start with endpoint(s) business-cards and peppol-lookups and skip incremental loading for now. 
Place the code in peppol_tools_pipeline.py and name the pipeline peppol_tools_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python peppol_tools_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Basic Authentication is required. Send credentials via the Authorization header using the Basic scheme format (base64-encoded username:password). Additionally, an accessToken query parameter must be provided in the request URL. Example: Authorization header with "Basic eW91cnVzZXI6eW91cnBhc3N3b3Jk" and accessToken=744166c1837144b1a44f5b3c418d500a as a query parameter.

    To get the appropriate API keys, please visit the original source at ademico-software.com. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python peppol_tools_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline peppol_tools load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset peppol_tools_data
The duckdb destination used duckdb:/peppol_tools.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline peppol_tools_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("peppol_tools_pipeline").dataset()
# get ["business-cards"] table as Pandas frame
data.["business-cards"].df().head()

Extra resources:

Next steps