Load WATS data in Python using dltHub

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

In this guide, we'll set up a complete WATS 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 wats_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://yoursubdomain.skywats.com/api/",
            "auth": {
                "type": "basic",
                "username": "token",
                "password": "authentication_token",
            },
        },
        "resources": [
            Package/UploadFile, Software/Package/UploadZip
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='wats_pipeline',
        destination='duckdb',
        dataset_name='wats_data', 
    )
    # Load the data
    load_info = pipeline.run(wats_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 wats’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 Upload: Upload files and ZIP packages to the system by package ID
  • File Attributes: Manage and configure file attributes via POST requests
  • Package Status: Update and retrieve package status with version and status parameters
  • General API Access: Base API endpoints for retrieving system data and resources

You will then debug the WATS 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 WATS support.

    dlt init dlthub:wats 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 WATS API, as specified in @wats-docs.yaml 
Start with endpoint(s) Package/UploadFile and Software/Package/UploadZip and skip incremental loading for now. 
Place the code in wats_pipeline.py and name the pipeline wats_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 wats_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    All API requests must be authenticated using basic authentication. Tokens are managed from within the WATS Control Panel and should be used as credentials for basic auth. Refer to the WATS Control Panel token administration for obtaining and managing authentication tokens.

    To get the appropriate API keys, please visit the original source at support.virinco.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 wats_pipeline.py

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

    Pipeline wats load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wats_data
The duckdb destination used duckdb:/wats.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 wats_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("wats_pipeline").dataset()
# get ["Package/UploadFile"] table as Pandas frame
data.["Package/UploadFile"].df().head()

Extra resources:

Next steps