Load ShopWired data in Python using dltHub

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

In this guide, we'll set up a complete ShopWired 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 shopwired_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.shopwired.co.uk/",
            "auth": {
                "type": "apikey",
                "token": api_key,
            },
        },
        "resources": [
            "countries",
            "pages",
            "products"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

Countries: Retrieve a list of countries available in the system.
Pages: Access various pages configured within the platform.
Products: Fetch details about products listed in the store.
Products Images: Get images associated with products.
Choice Sets: Manage and retrieve choice sets available for products.
Products Reviews: Access reviews for the products.
Product Options: Fetch different options available for products.
Products Variations: Get variations for products such as sizes or colors.
Customers: Retrieve customer data from the store.
Orders: Access order information and details.
Shipping Zones: Get information about configured shipping zones.
Events: Retrieve events related to the store.
Categories: Access product categories defined in the store.
Themes: Manage and retrieve theme settings for the store.

You will then debug the ShopWired 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:

We suggest using a model like Claude 3.7 Sonnet or better
Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
Read our full steps on setting up Cursor

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install dlt[workspace]
```
Initialize a dlt pipeline with ShopWired support.
```
dlt init dlthub:shopwired duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for ShopWired API, as specified in @shopwired-docs.yaml 
Start with endpoints "countries" and "pages" and skip incremental loading for now. 
Place the code in shopwired_pipeline.py and name the pipeline shopwired_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 shopwired_pipeline.py and await further instructions.

🔒 Set up credentials

The authentication method used requires an API key to be passed in the header as 'Authorization'. This key must be kept confidential.

To get the appropriate API keys, please visit the original source at https://www.shopwired.co.uk/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python shopwired_pipeline.py

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


Pipeline shopwired load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset shopwired_data
The duckdb destination used duckdb:/shopwired.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

📈 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 shopwired_pipeline show --dashboard
```
🐍 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("shopwired_pipeline").dataset()
# get "countries" table as Pandas frame
data.countries.df().head()
```

Running into errors?

Ensure that the API key and secret are correct to avoid '401 Unauthorized' errors. Some streams might not support full synchronization, and there could be rate limits in place that may result in 'REQUEST_LIMIT_EXCEEDED' errors. It's important to throttle API calls if necessary.

Extra resources:

Learn more with our 1h LLM-assisted coding course!