Load Kitsu data in Python using dltHub

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

In this guide, we'll set up a complete Kitsu 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 kitsu_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://kitsu.io/api/edge/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            anime, users
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Anime: Retrieve anime titles with filtering, sorting, and pagination support; includes related categories and media relationships
  • Users: Access user profiles with customizable fields like name and creation date; supports sorting by followers and following counts
  • OAuth: Authentication endpoint for secure user login and authorization
  • Pagination: Results support limit and offset parameters for browsing large datasets in manageable chunks

You will then debug the Kitsu 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 Kitsu support.

    dlt init dlthub:kitsu 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 Kitsu API, as specified in @kitsu-docs.yaml 
Start with endpoint(s) anime and users and skip incremental loading for now. 
Place the code in kitsu_pipeline.py and name the pipeline kitsu_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 kitsu_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Kitsu uses OAuth 2 for authentication. Most public GET endpoints do not require authentication, but NSFW/R18 content is hidden for unauthenticated requests. The OAuth2 endpoint is at https://kitsu.io/api/oauth. Request headers must include Content-Type as either application/json or application/x-www-form-urlencoded. Apps require a client_id and client_secret; currently, these can be omitted, empty strings, or use the provided test values: CLIENT_ID dd031b32d2f56c990b1425efe6c42ad847e7fe3ab46bf1299f05ecd856bdb7dd and CLIENT_SECRET 54d7307928f63414defd96399fc31ba847961ceaecef3a5fd93144e960c0e151. The Authorization Code grant type is defined for web server, browser-based, and mobile apps but is not yet implemented.

    To get the appropriate API keys, please visit the original source at kitsu.docs.apiary.io. 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 kitsu_pipeline.py

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

    Pipeline kitsu load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kitsu_data
The duckdb destination used duckdb:/kitsu.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 kitsu_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("kitsu_pipeline").dataset()
# get anime table as Pandas frame
data.anime.df().head()

Extra resources:

Next steps