Load Tesla data in Python using dltHub

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

In this guide, we'll set up a complete Tesla 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 tesla_fleet_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://developer.tesla.com/api/1/",
            "auth": {
                "type": "bearer",
                "token": "partner_authentication_token",
            },
        },
        "resources": [
            partner_accounts/fleet_telemetry_error_vins, partner_accounts/fleet_telemetry_errors
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Fleet Telemetry: Retrieve telemetry errors and error VINs for fleet vehicles
  • Partner Accounts: Manage partner account creation and public key retrieval for domain verification
  • Vehicle Commands: Execute commands on vehicles including trunk actuation and charge scheduling

You will then debug the Tesla 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 Tesla support.

    dlt init dlthub:tesla_fleet_api 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 Tesla API, as specified in @tesla_fleet_api-docs.yaml 
Start with endpoint(s) partner_accounts/fleet_telemetry_error_vins and partner_accounts/fleet_telemetry_errors and skip incremental loading for now. 
Place the code in tesla_fleet_api_pipeline.py and name the pipeline tesla_fleet_api_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 tesla_fleet_api_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Partner authentication token is required for certain endpoints. The token should be sent as a bearer token in the Authorization header. Specific details on how to obtain and use the partner authentication token are referenced in the partner-tokens documentation.

    To get the appropriate API keys, please visit the original source at developer.tesla.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 tesla_fleet_api_pipeline.py

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

    Pipeline tesla_fleet_api load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tesla_fleet_api_data
The duckdb destination used duckdb:/tesla_fleet_api.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 tesla_fleet_api_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("tesla_fleet_api_pipeline").dataset()
# get ["partner_accounts/fleet_telemetry_error_vins"] table as Pandas frame
data.["partner_accounts/fleet_telemetry_error_vins"].df().head()

Extra resources:

Next steps