Load Altium Designer data in Python using dltHub

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

In this guide, we'll set up a complete Altium DXP Developer System 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 altium_dxp_developer_system_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.altium.com/documentation/altium-dxp-developer/system-api/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            View, Kind, Count
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • View: Access and manage different views in the system.
  • Kind: Retrieve information about various kinds of documents.
  • Count: Obtain counts of specific elements or documents.

You will then debug the Altium DXP Developer System 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 Altium DXP Developer System support.

    dlt init dlthub:altium_dxp_developer_system 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 Altium DXP Developer System API, as specified in @altium_dxp_developer_system-docs.yaml 
Start with endpoints View and Kind and skip incremental loading for now. 
Place the code in altium_dxp_developer_system_pipeline.py and name the pipeline altium_dxp_developer_system_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 altium_dxp_developer_system_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The source uses an API key for authentication. Each token is valid for a period of three months and must be regenerated after it expires. Users need to ensure that the token is valid and check its expiration to avoid unauthorized requests.

    To get the appropriate API keys, please visit the original source at https://www.altium.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 altium_dxp_developer_system_pipeline.py

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

    Pipeline altium_dxp_developer_system load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset altium_dxp_developer_system_data
The duckdb destination used duckdb:/altium_dxp_developer_system.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 altium_dxp_developer_system_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("altium_dxp_developer_system_pipeline").dataset()
# get ie table as Pandas frame
data.ie.df().head()

Running into errors?

Users must be aware that the Altium Designer SDK is currently in the Beta phase, meaning features and documentation are evolving. Additionally, various limitations and dependencies exist, such as the necessity for Microsoft Excel and Access to be installed properly. Users may also encounter issues related to token expiration and invalid token errors if not monitored closely.

Extra resources:

Next steps