Load Invision Community data in Python using dltHub

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

In this guide, we'll set up a complete Invision Community 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 invision_community_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.example.com/ips4/api",
            "auth": {
                "type": "basic",
                "username": "api_key",
                "password": ""
            },
        },
        "resources": [
            core/hello, core/me
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Core: Basic API health check and user authentication endpoints
  • Profile: User profile information retrieval
  • Courses: Browse and manage course listings, enrollments, and course details
  • Forums: Create and manage forum discussions and topics
  • Gallery: Review management with reaction/interaction capabilities

You will then debug the Invision Community 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 Invision Community support.

    dlt init dlthub:invision_community 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 Invision Community API, as specified in @invision_community-docs.yaml 
Start with endpoint(s) core/hello and core/me and skip incremental loading for now. 
Place the code in invision_community_pipeline.py and name the pipeline invision_community_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 invision_community_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    API Key authentication is sent via HTTP Basic Auth with the API key as the username and no password. OAuth access tokens are alternatively available and provide user-specific access; some endpoints require one method or the other. API Keys are generated in AdminCP under System → REST & OAuth → API Keys and must be configured per endpoint. Client Credentials OAuth grant is also supported as an alternative to API Keys for full API access.

    To get the appropriate API keys, please visit the original source at invisioncommunity.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 invision_community_pipeline.py

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

    Pipeline invision_community load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset invision_community_data
The duckdb destination used duckdb:/invision_community.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 invision_community_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("invision_community_pipeline").dataset()
# get ["core/hello"] table as Pandas frame
data.["core/hello"].df().head()

Extra resources:

Next steps