Load Modern Treasury data in Python using dltHub

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

In this guide, we'll set up a complete Modern Treasury 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 modern_treasury_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.moderntreasury.com/api/v",
            {'auth': {'type': 'bearer', 'token': 'access_token'}},
        },
        "resources": [
            ping,,users,,invoices
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • User Management: Manage users within the application.
  • Payment Management: Create and manage payments, including bulk operations.
  • Invoice Management: Handle invoices and associated line items.
  • Ledger Management: Manage ledgers and associated transactions.
  • Connection Management: Manage and establish connections with external banking services.

You will then debug the Modern Treasury 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 Modern Treasury support.

    dlt init dlthub:modern_treasury_migrations 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 Modern Treasury API, as specified in @modern_treasury_migrations-docs.yaml 
Start with endpoints ping and  and skip incremental loading for now. 
Place the code in modern_treasury_migrations_pipeline.py and name the pipeline modern_treasury_migrations_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 modern_treasury_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is performed using OAuth2 with a bearer token. It requires setting up a connected app in the API and includes a specific organization ID and API key for authentication.

    To get the appropriate API keys, please visit the original source at https://www.moderntreasury.com/. 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 modern_treasury_migrations_pipeline.py

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

    Pipeline modern_treasury_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset modern_treasury_migrations_data
The duckdb destination used duckdb:/modern_treasury_migrations.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 modern_treasury_migrations_pipeline show --dashboard

  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("modern_treasury_migrations_pipeline").dataset()
# get in table as Pandas frame
data.in.df().head()

Running into errors?

Be aware that not all banks support intraday reporting, and the API has rate limits that can affect performance. Additionally, certain operations are restricted in the sandbox environment, and care must be taken to handle sensitive data appropriately. Multi-factor authentication is enabled by default, and it cannot be turned off once activated.

Extra resources:

Next steps

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

- User Management: Manage users within the application.
- Payment Management: Create and manage payments, including bulk operations.
- Invoice Management: Handle invoices and associated line items.
- Ledger Management: Manage ledgers and associated transactions.
- Connection Management: Manage and establish connections with external banking services.

You will then debug the Modern Treasury 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

```default
Before getting started, let's make sure Cursor is set up correctly:
   - We suggest using a model like Claude 3.7 Sonnet or better
   - Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as **@dlt rest api**
   - [Read our full steps on setting up Cursor](https://dlthub.com/docs/dlt-ecosystem/llm-tooling/cursor-restapi#23-configuring-cursor-with-documentation)

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 Modern Treasury support.

    dlt init dlthub:modern_treasury_migrations 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 Modern Treasury API, as specified in @modern_treasury_migrations-docs.yaml 
Start with endpoints ping and  and skip incremental loading for now. 
Place the code in modern_treasury_migrations_pipeline.py and name the pipeline modern_treasury_migrations_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 modern_treasury_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is performed using OAuth2 with a bearer token. It requires setting up a connected app in the API and includes a specific organization ID and API key for authentication.

    To get the appropriate API keys, please visit the original source at https://www.moderntreasury.com/. 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 modern_treasury_migrations_pipeline.py

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

    Pipeline modern_treasury_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset modern_treasury_migrations_data
The duckdb destination used duckdb:/modern_treasury_migrations.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 modern_treasury_migrations_pipeline show --dashboard

  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("modern_treasury_migrations_pipeline").dataset()
# get in table as Pandas frame
data.in.df().head()

Running into errors?

Be aware that not all banks support intraday reporting, and the API has rate limits that can affect performance. Additionally, certain operations are restricted in the sandbox environment, and care must be taken to handle sensitive data appropriately. Multi-factor authentication is enabled by default, and it cannot be turned off once activated.

Extra resources:

Next steps