Load INVERS CloudBoxx data in Python using dltHub

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

In this guide, we'll set up a complete INVERS OneAPI 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 invers_oneapi_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.invers.com/v",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            vehicles,vehicle-commands,vehicle-management
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Vehicles: Retrieve and manage vehicle data including states, tags, and capabilities.
  • Vehicle Commands: Send commands to vehicles for various operations.
  • Vehicle Management: Manage vehicles including sharing and configuration.
  • Vehicle Events: Access asynchronous events related to vehicle states.

You will then debug the INVERS OneAPI 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 INVERS OneAPI support.

    dlt init dlthub:invers_oneapi_migrations 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 INVERS OneAPI API, as specified in @invers_oneapi_migrations-docs.yaml 
Start with endpoints vehicles and vehicle-commands and skip incremental loading for now. 
Place the code in invers_oneapi_migrations_pipeline.py and name the pipeline invers_oneapi_migrations_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 invers_oneapi_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The INVERS OneAPI requires OAuth2 authentication, which means that a valid access token must be included in the request header under the 'Authorization' field.

    To get the appropriate API keys, please visit the original source at https://www.invers.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 invers_oneapi_migrations_pipeline.py

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

    Pipeline invers_oneapi_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset invers_oneapi_migrations_data
The duckdb destination used duckdb:/invers_oneapi_migrations.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 invers_oneapi_migrations_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("invers_oneapi_migrations_pipeline").dataset()
# get ehicle table as Pandas frame
data.ehicle.df().head()

Running into errors?

When using the API, avoid sending requests too frequently to prevent throttling. Ensure that the access token is valid and has the necessary scopes for the requested operations. Each vehicle has unique identifiers, and certain commands may be restricted based on the vehicle's state or configuration.

Extra resources:

Next steps