Load Musoni System data in Python using dltHub

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

In this guide, we'll set up a complete Musoni 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 musoni_system_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://dev.irl.musoniservices.com:8443/api/v1/reports/prepared",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "batch", "searchtemplate"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='musoni_system_pipeline',
        destination='duckdb',
        dataset_name='musoni_system_data', 
    )
    # Load the data
    load_info = pipeline.run(musoni_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 musoni_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:

  • Client Identifier Validation: Validate client identifiers using a specified ID.
  • Surveys: Access or manage surveys by their unique names.
  • Savings Products: Retrieve information about available savings products.
  • Collection Sheet: Manage or retrieve collection sheets for data collection.
  • Hooks: Handle webhook events; includes specific hook operations using ID.
  • Accounting Exports: Export accounting data for reporting or analysis.
  • Data Filters: Apply filters based on specific metadata model types.
  • Withdrawal Sheet: Manage withdrawal requests; can include approval commands.
  • Credit Bureaus: Access data from specific credit bureaus using their ID.
  • Templates: Retrieve or manage templates for various operational needs.
  • Data Export: Prepare data exports for specific use cases.
  • Reports: Generate or manage prepared batch reports.
  • Funds: Access information regarding available funds.
  • Data Exports: Handle exports related to specific base entities.
  • Batches: Manage or retrieve batch processes.
  • Search: Perform searches across available data.
  • Holidays: Access information regarding holidays.
  • Client Identifier Type: Manage or retrieve types of client identifiers.

You will then debug the Musoni 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 Musoni System support.

    dlt init dlthub:musoni_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 Musoni System API, as specified in @musoni_system-docs.yaml 
Start with endpoints batch and searchtemplate and skip incremental loading for now. 
Place the code in musoni_system_pipeline.py and name the pipeline musoni_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 musoni_system_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Usage of the API requires a user-specific API key which can be requested via the Musoni Service Desk.

    To get the appropriate API keys, please visit the original source at https://demo.musonisystem.com/api-docs/apiLive.htm. 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 musoni_system_pipeline.py

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

    Pipeline musoni_system load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset musoni_system_data
The duckdb destination used duckdb:/musoni_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 musoni_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("musoni_system_pipeline").dataset()
# get "batch" table as Pandas frame
data."batch".df().head()

Extra resources:

Next steps