Load Financial Line data in Python using dltHub

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

In this guide, we'll set up a complete Financial Line 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 financial_line_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.finline.io/api/v1/checkout",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "baf1592f-d7e8-4c28-9b86-43499bc54904", "..."
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

Public Receipt: Endpoints for accessing specific receipt information using a unique identifier.
API v1 Checkout: Endpoints for processing checkout actions, including forms and QR code generation.
API v1 Payment: Endpoints for handling payment transactions, including options for QR payments.
API v1 POS: Endpoints related to point-of-sale functionalities, requiring a POS ID.
Streaming Report: Endpoints for fetching and starting streaming reports, typically for real-time data retrieval.
API v1 Refund: Endpoints for processing refund requests related to transactions.
API v1 Recurring: Endpoints to manage recurring payments, including cancellation options.

You will then debug the Financial Line 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:

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

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install "dlt[workspace]"
```
Initialize a dlt pipeline with Financial Line support.
```
dlt init dlthub:financial_line duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for Financial Line API, as specified in @financial_line-docs.yaml 
Start with endpoints baf1592f-d7e8-4c28-9b86-43499bc54904 and ... and skip incremental loading for now. 
Place the code in financial_line_pipeline.py and name the pipeline financial_line_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 financial_line_pipeline.py and await further instructions.

🔒 Set up credentials

To authenticate, you need an API key (API_KEY), which should be included in the header as X-API-KEY, and an API secret (API_SECRET), which must be combined with the API key in the X-API-AUTH header as CPAY ${API_KEY}:${API_SECRET}; ensure you also set the Content-Type header to application/json.

To get the appropriate API keys, please visit the original source at https://docs.finline.io/transactions-purchase/api/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python financial_line_pipeline.py

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


Pipeline financial_line load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset financial_line_data
The duckdb destination used duckdb:/financial_line.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

📈 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 financial_line_pipeline show
```
🐍 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("financial_line_pipeline").dataset()
# get "baf1592f-d7e8-4c28-9b86-43499bc54904" table as Pandas frame
data."baf1592f-d7e8-4c28-9b86-43499bc54904".df().head()
```

Extra resources:

Learn more with our 1h LLM-assisted coding course!