Load Worldpay Vantiv data in Python using dltHub

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

In this guide, we'll set up a complete Worldpay Vantiv 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 worldpay_vantiv_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.justt.ai/v1/",
            "auth": {
                "type": "bearer",
                "token": rest_api_token,
            },
        },
        "resources": [
            chargebacks, extra-data
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Chargebacks Management: Retrieve, create, and update chargeback records with specific chargeback IDs
  • GraphQL Queries: Execute merchant-related GraphQL queries for flexible data retrieval
  • Extra Data: Access additional data through dedicated extra-data endpoints
  • General API Operations: Core GET and POST operations for standard API interactions

You will then debug the Worldpay Vantiv 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 Worldpay Vantiv support.

    dlt init dlthub:worldpay_vantiv 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 Worldpay Vantiv API, as specified in @worldpay_vantiv-docs.yaml 
Start with endpoint(s) chargebacks and extra-data and skip incremental loading for now. 
Place the code in worldpay_vantiv_pipeline.py and name the pipeline worldpay_vantiv_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 worldpay_vantiv_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    REST API token generated from the Customer Hub at https://app.justt.ai/connect-psp/integration/api-integration. The new REST API requires a different token than the previous GraphQL API. Send the token as authentication when making requests to https://api.justt.ai/v1/. Generate a new token from the Customer Hub before migration.

    To get the appropriate API keys, please visit the original source at developers.justt.ai. 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 worldpay_vantiv_pipeline.py

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

    Pipeline worldpay_vantiv load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset worldpay_vantiv_data
The duckdb destination used duckdb:/worldpay_vantiv.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 worldpay_vantiv_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("worldpay_vantiv_pipeline").dataset()
# get chargebacks table as Pandas frame
data.chargebacks.df().head()

Extra resources:

Next steps