Load Random Nerd Tutorials data in Python using dltHub

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

In this guide, we'll set up a complete Random Nerd Tutorials 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 random_nerd_tutorials_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://api.openweathermap.org/data/2.5/",
            "auth": {
                "type": "basic",
                "username": "admin",
                "password": "admin",
            },
        },
        "resources": [
            weather, logged-out
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Weather Data: Retrieve current weather conditions, forecasts, and meteorological information for specific locations
  • Sensor Management: Create, read, and update sensor data and IoT device readings
  • Authentication: Handle user login, logout, and session management endpoints
  • Data Updates: Submit and store new data points from connected devices or sensors
  • Channel Access: Retrieve and manage data channels for organizing IoT data streams

You will then debug the Random Nerd Tutorials 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 Random Nerd Tutorials support.

    dlt init dlthub:random_nerd_tutorials 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 Random Nerd Tutorials API, as specified in @random_nerd_tutorials-docs.yaml 
Start with endpoint(s) weather and logged-out and skip incremental loading for now. 
Place the code in random_nerd_tutorials_pipeline.py and name the pipeline random_nerd_tutorials_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 random_nerd_tutorials_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    HTTP Basic Authentication is used. The server requires a username and password for access. Default credentials are username "admin" and password "admin". A 401 response code is returned for unauthorized requests, prompting the client to provide credentials. The logout endpoint returns 401 to clear authentication state.

    To get the appropriate API keys, please visit the original source at randomnerdtutorials.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 random_nerd_tutorials_pipeline.py

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

    Pipeline random_nerd_tutorials load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset random_nerd_tutorials_data
The duckdb destination used duckdb:/random_nerd_tutorials.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 random_nerd_tutorials_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("random_nerd_tutorials_pipeline").dataset()
# get weather table as Pandas frame
data.weather.df().head()

Extra resources:

Next steps