Load E-Com Processing data in Python using dltHub

Build a E-Com Processing-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete E-Com Processing 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 e_com_processing_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://staging.api.e-comprocessing.net/payee/owners/:owner_unique_id",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "documents", "documents`"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

Reconciliation Endpoints:
- /reconcile: Endpoint for reconciling transactions.
- /reconcile/by_date: Endpoint for reconciling transactions by a specific date.
Payee Endpoints:
- /payee: Endpoint for accessing general payee information.
- /payee/:payee_unique_id: Endpoint for accessing specific payee details.
- /payee/:payee_unique_id/documents: Endpoint for accessing documents related to a specific payee.
- /payee/:payee_unique_id/documents/:document_unique_id: Endpoint for accessing a specific document of a payee.
- /payee/:payee_unique_id/account: Endpoint for accessing accounts related to a specific payee.
- /payee/:payee_unique_id/account/:account_unique_id: Endpoint for accessing a specific account of a payee.
- /payee/:payee_unique_id/verifications: Endpoint for accessing verifications related to a specific payee.
- /payee/:payee_unique_id/verifications/:verification_unique_id: Endpoint for accessing a specific verification of a payee.
Payee Owner Endpoints:
- /payee/owners: Endpoint for accessing information about owners of payees.
- /payee/owners/:owner_unique_id: Endpoint for accessing specific owner details.
- /payee/owners/:owner_unique_id/owners: Endpoint for accessing sub-owners related to a specific owner.
- /payee/owners/:owner_unique_id/documents: Endpoint for accessing documents related to a specific owner.
- /payee/owners/:owner_unique_id/documents/:document_unique_id: Endpoint for accessing a specific document of an owner.
Billing Transactions Endpoints:
- /billing_transactions/v1/graphql: Endpoint for accessing billing transactions via GraphQL.
Billing Statements Endpoints:
- /billing_statements/v1/graphql: Endpoint for accessing billing statements via GraphQL.
Transactions Endpoint:
- /transactions: Endpoint for accessing transaction details.

You will then debug the E-Com Processing 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 E-Com Processing support.
```
dlt init dlthub:e_com_processing 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 E-Com Processing API, as specified in @e_com_processing-docs.yaml 
Start with endpoints documents and documents` and skip incremental loading for now. 
Place the code in e_com_processing_pipeline.py and name the pipeline e_com_processing_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 e_com_processing_pipeline.py and await further instructions.

🔒 Set up credentials

Auth information not found.

To get the appropriate API keys, please visit the original source at https://e-comprocessing.github.io/gateway-api-docs/. 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 e_com_processing_pipeline.py

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


Pipeline e_com_processing load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset e_com_processing_data
The duckdb destination used duckdb:/e_com_processing.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 e_com_processing_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("e_com_processing_pipeline").dataset()
# get "documents" table as Pandas frame
data."documents".df().head()
```

Extra resources:

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