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Load LVGL ThorVG data in Python using dltHub

Build a LVGL ThorVG-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

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In this guide, we'll set up a complete LVGL 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 lvgl_thorvg_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://github.com/lvgl/lvgl/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "api", "misc", "lvgl"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='lvgl_thorvg_pipeline',
        destination='duckdb',
        dataset_name='lvgl_thorvg_data', 
    )
    # Load the data
    load_info = pipeline.run(lvgl_thorvg_source())
    print(load_info)

Why use dlt 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 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 LVGL ThorVG's API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • api: Contains core functionalities for accessing various resources.
  • misc: Miscellaneous utilities and functionalities.
  • lvgl: Specific functionalities related to the LVGL library.
  • chart: Endpoints related to chart functionalities.
  • lv_fs: Endpoints for file system operations.
  • image: Operations related to image handling.
  • window: Functions to manage windows.
  • button: Endpoints for button functionalities.
  • switch: Endpoints to manage switches.
  • widgets: Access to various widget functionalities.
  • spinner: Specific functionalities for spinner elements.
  • lv_draw: Drawing functionalities and operations.
  • lv_anim: Endpoints related to animations.
  • keyboard: Keyboard functionalities and operations.
  • calendar: Calendar-related functionalities.
  • lv_indev: Endpoints for input device management.
  • tileview: Functionalities related to tile views.
  • dropdown: Endpoints for dropdown functionalities.

You will then debug the LVGL 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, set up a virtual environment (instructions) and install the dlt workspace:


uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

Now you're ready to get started!

  1. Install the dlt AI Workbench

    Configure the workbench for your coding assistant:

    dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

    This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent.

    Learn more about the dltHub AI Workbench and setup details for each assistant →

  2. Install the rest-api-pipeline toolkit

    The AI Workbench provides different toolkits for each phase of the data engineering lifecycle. To start you need to install the rest-api-pipeline toolkit:

    dlt ai toolkit rest-api-pipeline install

    This loads different skills and contexts about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. Importantly, it does not need to ask you for credentials directly. In dlt, API credentials are provided via a secrets.toml file (learn more about secrets management →), and the agent should use the MCP tools to see their shape and detect misconfigurations. It never needs to access the file directly.

    Learn more about the rest-api-pipeline toolkit →

  3. Start LLM-assisted coding

    Here's a prompt to get you started:

    Prompt
    Use /find-source to load data from the LVGL ThorVG API into DuckDB.

    The AI Workbench rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and then follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

  4. View the result

    After the rest-api-pipeline workflow has finished, you will end up with a working REST API source with validated endpoints and a pipeline that writes data into a local dataset you have inspected and verified.

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

    By launching the Pipeline Dashboard, you can see various information about the pipeline and the loaded data

    • Pipeline overview: State, load metrics
    • Data's schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline lvgl_thorvg_pipeline show

Running into errors?

The LVGL library is not thread-safe, so proper mutex handling is critical. Additionally, memory usage may vary based on architecture and compiler settings, and some objects may return nulls in deeply nested fields. It is essential to ensure that the connected app is correctly set up for OAuth2 authentication to avoid '401 Unauthorized' errors. Be cautious with the API call limits to avoid throttling errors.

Next steps

You can go to the next phases of your data engineering journey by handing over to other toolkits of the dltHub AI Workbench:

  • data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
  • dlthub-runtime — Deploy, schedule, and monitor your pipeline in production
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

Or explore the following resources for more information:

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