Load Modica Group data in Python using dltHub

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

In this guide, we'll set up a complete Modica Group 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 modica_group_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.modicagroup.com/rest/",
            "auth": {
                "type": "basic",
                "username": "application_name",
                "password": "password",
            },
        },
        "resources": [
            gateway, gateway/messages/int:message-id
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='modica_group_pipeline',
        destination='duckdb',
        dataset_name='modica_group_data', 
    )
    # Load the data
    load_info = pipeline.run(modica_group_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 modica_group’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • 2FA (Two-Factor Authentication): Manage authentication challenges and verification processes
  • Campaign Manager: Create, retrieve, update, and delete campaign lists and applications
  • Gateway: Access and manage messages through the gateway system
  • Messages: Retrieve specific messages by ID and view message details

You will then debug the Modica Group 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 Modica Group support.

    dlt init dlthub:modica_group 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 Modica Group API, as specified in @modica_group-docs.yaml 
Start with endpoint(s) gateway and gateway/messages/int:message-id and skip incremental loading for now. 
Place the code in modica_group_pipeline.py and name the pipeline modica_group_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 modica_group_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    HTTP Basic Authentication is required for all requests. Provide a username and password in the Authorization header using the format Authorization: Basic base64(username:password), where the username is your application name from the Modica configuration page. A 401 response indicates missing or incorrect credentials or insufficient permissions. Contact support@modicagroup.com if you need credentials.

    To get the appropriate API keys, please visit the original source at omni.modicagroup.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 modica_group_pipeline.py

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

    Pipeline modica_group load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset modica_group_data
The duckdb destination used duckdb:/modica_group.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 modica_group_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("modica_group_pipeline").dataset()
# get gateway table as Pandas frame
data.gateway.df().head()

Extra resources:

Next steps