Load Cherwell Car Wash API data in Python using dltHub

Build a Cherwell Car Wash API-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Cherwell Car Wash API 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 cherwell_car_wash_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://carwash.ambrosebuilding.com.au/cherwellapi",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            token
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='cherwell_car_wash_api_pipeline',
        destination='duckdb',
        dataset_name='cherwell_car_wash_api_data', 
    )
    # Load the data
    load_info = pipeline.run(cherwell_car_wash_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 cherwell_car_wash_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:

  • Authentication: POST /token - Obtain access tokens for API authentication
  • Car Wash Services: Manage car wash bookings, schedules, and service details
  • Customer Management: Handle customer profiles and account information
  • Payments: Process transactions and manage billing
  • Reporting: Access wash history, analytics, and service reports

You will then debug the Cherwell Car Wash API 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 Cherwell Car Wash API support.

    dlt init dlthub:cherwell_car_wash_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 Cherwell Car Wash API API, as specified in @cherwell_car_wash_api-docs.yaml 
Start with endpoint(s) token and skip incremental loading for now. 
Place the code in cherwell_car_wash_api_pipeline.py and name the pipeline cherwell_car_wash_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 cherwell_car_wash_api_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The REST API supports four authentication modes: Internal (login ID and password), LDAP (LDAP credentials or domain\username and password), SAML (SAML-validated credentials), and Windows (LOGON_USER or domain\username and password). To authenticate, make an HTTP POST call to the token operation with credentials in the request body according to your authentication type. The response includes an access_token field with token_type "bearer", expires_in duration in seconds, and a refresh_token for token renewal. Use the access_token as a bearer token in subsequent API requests.

    To get the appropriate API keys, please visit the original source at carwash.ambrosebuilding.com.au. 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 cherwell_car_wash_api_pipeline.py

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

    Pipeline cherwell_car_wash_api load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cherwell_car_wash_api_data
The duckdb destination used duckdb:/cherwell_car_wash_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 cherwell_car_wash_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("cherwell_car_wash_api_pipeline").dataset()
# get token table as Pandas frame
data.token.df().head()

Extra resources:

Next steps