Load Keygen data in Python using dltHub

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

In this guide, we'll set up a complete Keygen 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 keygen_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.keygen.sh/v1/accounts/demo",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "engines", "machines"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Account Management: Endpoints related to user accounts and their details, such as managing account settings and retrieving specific account information.
  • Releases: Endpoints for accessing and upgrading to specific versions of software releases.
  • Machines: Endpoints for managing and retrieving information about machines associated with an account.
  • Engines: Endpoints for interacting with different engines, specifically for Tauri applications and their configurations.
  • Artifacts: Endpoints for accessing application artifacts, such as appcast files for updates.

You will then debug the Keygen 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 Keygen support.

    dlt init dlthub:keygen 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 Keygen API, as specified in @keygen-docs.yaml 
Start with endpoints engines and machines and skip incremental loading for now. 
Place the code in keygen_pipeline.py and name the pipeline keygen_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 keygen_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    To authenticate, you need a key which is the 'License C1B6DE-39A6E3-DE1529-8559A0-4AF593-V3', and you should apply it in the header with the name 'Authorization' when making requests to the API at the URL 'https://api.keygen.sh/v1/accounts/demo/releases/1.9.2/upgrade'.

    To get the appropriate API keys, please visit the original source at https://keygen.sh/docs/api/rate-limiting/. 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 keygen_pipeline.py

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

    Pipeline keygen load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset keygen_data
The duckdb destination used duckdb:/keygen.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 keygen_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("keygen_pipeline").dataset()
# get "engines" table as Pandas frame
data."engines".df().head()

Extra resources:

Next steps