Load OverDrive data in Python using dltHub

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

In this guide, we'll set up a complete OverDrive 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 overdrive_patron_authentication_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://oauth-patron.overdrive.com/patrontoken",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "refresh", "code"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • /patrontoken: Endpoint for managing patron tokens, likely used for authentication or session management.
  • /device/initiate: Endpoint for initiating a device connection, may include a redirect URL for navigation after the process.
  • /patrontoken/code: Endpoint related to patron token verification or code handling, potentially for security checks.
  • /patrontoken/refresh: Endpoint for refreshing an existing patron token, ensuring continued access or session validity.

You will then debug the OverDrive 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 OverDrive support.

    dlt init dlthub:overdrive_patron_authentication 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 OverDrive API, as specified in @overdrive_patron_authentication-docs.yaml 
Start with endpoints refresh and code and skip incremental loading for now. 
Place the code in overdrive_patron_authentication_pipeline.py and name the pipeline overdrive_patron_authentication_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 overdrive_patron_authentication_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The snippets mention that to access the OverDrive API, you need to request access, and specific authentication methods are outlined, including client authentication and patron authentication, which can be found in the API documentation at /api-docs/authentication.

    To get the appropriate API keys, please visit the original source at https://developer.overdrive.com/api-docs/authentication/patron-authentication. 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 overdrive_patron_authentication_pipeline.py

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

    Pipeline overdrive_patron_authentication load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset overdrive_patron_authentication_data
The duckdb destination used duckdb:/overdrive_patron_authentication.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 overdrive_patron_authentication_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("overdrive_patron_authentication_pipeline").dataset()
# get "refresh" table as Pandas frame
data."refresh".df().head()

Extra resources:

Next steps