Load Sikt Maskinporten data in Python using dltHub

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

In this guide, we'll set up a complete Sikt Maskinporten 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 sikt_maskinporten_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.nsd.no/",
            "auth": {
                "type": "bearer",
                "token": "access_token",
            },
        },
        "resources": [
            graphql, graphql
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • GraphQL API: Query and mutation endpoint for data operations at NSD
  • OpenID Configuration: Discovery endpoint for SSO authentication configuration
  • OAuth Token: Token generation and authentication endpoint for SSO
  • Version Endpoints: API version information across test, development, and production environments for Felles Student System
  • Person Management: REST endpoints for creating and managing person records
  • OIDC Authentication: OpenID Connect authentication service for identity verification

You will then debug the Sikt Maskinporten 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 Sikt Maskinporten support.

    dlt init dlthub:sikt_maskinporten 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 Sikt Maskinporten API, as specified in @sikt_maskinporten-docs.yaml 
Start with endpoint(s) graphql and graphql and skip incremental loading for now. 
Place the code in sikt_maskinporten_pipeline.py and name the pipeline sikt_maskinporten_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 sikt_maskinporten_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    OAuth 2.0 with client credentials grant type. Register a client application to obtain CLIENT_ID and CLIENT_SECRET, then POST to https://sso.nsd.no/oauth/token with HTTP Basic Auth (client_id:client_secret in Authorization header) and form data grant_type=client_credentials and scope=org:{organization_id}. The response contains an access_token JWT that is used to authenticate API requests.

    To get the appropriate API keys, please visit the original source at docs.sikt.no. 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 sikt_maskinporten_pipeline.py

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

    Pipeline sikt_maskinporten load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sikt_maskinporten_data
The duckdb destination used duckdb:/sikt_maskinporten.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 sikt_maskinporten_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("sikt_maskinporten_pipeline").dataset()
# get graphql table as Pandas frame
data.graphql.df().head()

Extra resources:

Next steps