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Load Lerian Studio data in Python using dltHub

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

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In this guide, we'll set up a complete Midaz Onboarding 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 lerian_studio_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://onboarding.sandbox.midaz.io/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "plugin-crm", "plugin-auth", "plugin-fees"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Plugin CRM: Integrates customer relationship management features.
  • Plugin Auth: Handles authentication processes.
  • Plugin Fees: Manages fee-related functionalities.
  • Midaz Console: Provides administrative controls and settings.
  • Plugin Identity: Manages user identity features.
  • Midaz Onboarding: Facilitates the onboarding process for users.
  • Midaz Transaction: Handles transaction-related operations.
  • Plugin Smart Templates: Offers smart template functionalities for various use cases.

You will then debug the Midaz Onboarding 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 Midaz Onboarding support.

    dlt init dlthub:lerian_studio 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 Midaz Onboarding API, as specified in @lerian_studio-docs.yaml 
Start with endpoints plugin-crm and  and skip incremental loading for now. 
Place the code in lerian_studio_pipeline.py and name the pipeline lerian_studio_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 lerian_studio_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is done using an API key.

    To get the appropriate API keys, please visit the original source at https://www.midaz.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 lerian_studio_pipeline.py

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

    Pipeline lerian_studio load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset lerian_studio_data
The duckdb destination used duckdb:/lerian_studio.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 lerian_studio_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

dataset = dlt.pipeline("lerian_studio_pipeline").dataset()
# Get plugin-crm table as Pandas DataFrame
df = dataset.table("plugin-crm").df().head()

Running into errors?

The sandbox is intended for learning and exploration purposes; data is mocked or non-persistent. Operations are idempotent, meaning they can be safely retried without risk of duplication. Additionally, pagination is limited to a maximum of 100 items per page by default. Ensure to include the required headers such as X-Organization-ID in requests.

Extra resources:

Next steps

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