Load ApiVerve data in Python using dltHub

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

In this guide, we'll set up a complete Verve 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 api_verve_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.apiverve.com/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            image,
            loan,
            leetspeak
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Image: Provides access to image-related functionalities.
  • Loan: Offers loan calculation and related services.
  • Chuck Norris: A fun endpoint for jokes about Chuck Norris.
  • Leetspeak: Converts text to leetspeak style.
  • Cities Lookup: Retrieves information about cities.
  • Silver Price: Provides current silver prices.
  • Human Name Parser: Parses human names into components.
  • Sentiment Analysis: Analyzes text for sentiment.
  • Word Rhymes: Finds rhyming words.

You will then debug the Verve 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 Verve support.

    dlt init dlthub:api_verve 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 Verve API, as specified in @api_verve-docs.yaml 
Start with endpoints image and loan and skip incremental loading for now. 
Place the code in api_verve_pipeline.py and name the pipeline api_verve_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 api_verve_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is done via an API key that must be included in the request header as 'X-API-Key'.

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

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

    Pipeline api_verve load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset api_verve_data
The duckdb destination used duckdb:/api_verve.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 api_verve_pipeline show --dashboard

  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("api_verve_pipeline").dataset()
# get mag table as Pandas frame
data.mag.df().head()

Running into errors?

Ensure that the API key is stored securely and included in all requests. Be aware of rate limits and implement exponential backoff when limits are exceeded. Some endpoints may return null for deeply nested fields, and it's important to verify the API key configuration before making requests.

Extra resources:

Next steps