Load Circutor Scout data in Python using dltHub

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

In this guide, we'll set up a complete Circutor Scout 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 circutor_scout_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://scout.circutor.com/api/v2/",
            "auth": {
                "type": "bearer",
                "token": app_token,
            },
        },
        "resources": [
            assets, assets/{assetId}/metricsv3/raw
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Assets: Retrieve asset information and details
  • Metrics (Raw): Get raw metric data for assets, with support for single and batch queries

You will then debug the Circutor Scout 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 Circutor Scout support.

    dlt init dlthub:circutor_scout 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 Circutor Scout API, as specified in @circutor_scout-docs.yaml 
Start with endpoint(s) assets and assets/{assetId}/metricsv3/raw and skip incremental loading for now. 
Place the code in circutor_scout_pipeline.py and name the pipeline circutor_scout_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 circutor_scout_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    App tokens are an alternative authentication method tied to an account for read/write operations. Tokens are created either from the website or via API and are used instead of username/password credentials to access Scout's data and perform operations on assets, devices, gateways, and metrics.

    To get the appropriate API keys, please visit the original source at scout.circutor.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 circutor_scout_pipeline.py

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

    Pipeline circutor_scout load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset circutor_scout_data
The duckdb destination used duckdb:/circutor_scout.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 circutor_scout_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("circutor_scout_pipeline").dataset()
# get assets table as Pandas frame
data.assets.df().head()

Extra resources:

Next steps