Load JSON Server data in Python using dltHub

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

In this guide, we'll set up a complete JSON Server 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 json_server_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://opentdb.com/",
            "auth": {
                "type": "bearer",
                "token": "your_jwt_token_here",
            },
        },
        "resources": [
            api.php, users/me
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • File Operations: Upload and manage files (POST /api/Main/UploadFile, DELETE operations)
  • User Management: Create, retrieve, and update user data (GET /user/, POST /user/, PUT /user/123, DELETE /user/123)
  • Authentication: Get current user information (GET /wp-json/users/me)
  • Trivia/Content: Fetch quiz questions and content data (GET /api.php with parameters)
  • Posts: Retrieve and manage posts (GET api_endpoint/posts)
  • General Endpoints: Various GET requests for retrieving general data

You will then debug the JSON Server 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 JSON Server support.

    dlt init dlthub:json_server 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 JSON Server API, as specified in @json_server-docs.yaml 
Start with endpoint(s) api.php and users/me and skip incremental loading for now. 
Place the code in json_server_pipeline.py and name the pipeline json_server_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 json_server_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    JSON Web Tokens (JWT) are used for authentication, with digitally signed tokens transmitted in both request and response headers. The server encodes access rights within the JWT, eliminating the need for database lookups. Authentication context varies: applications may be authenticated as clients with specific permissions, or the API may need to identify individual users and their rights depending on whether private user data is being accessed.

    To get the appropriate API keys, please visit the original source at www.sitepoint.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 json_server_pipeline.py

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

    Pipeline json_server load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset json_server_data
The duckdb destination used duckdb:/json_server.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 json_server_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("json_server_pipeline").dataset()
# get ["api.php"] table as Pandas frame
data.["api.php"].df().head()

Extra resources:

Next steps