Load Amazon Selling Partner API data in Python using dltHub

Build a Amazon Selling Partner API-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Selling Partner API 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 selling_partner_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://spapi.vip/v/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            type,,rates,,money
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Type: Provides general information about the API type.
  • Rates: Contains endpoints for accessing various rate-related data.
  • Money: Deals with financial transactions and related data.
  • Weight: Provides endpoints related to weight metrics.
  • Orders: Manages order-related data and operations.
  • Sellers: Contains information about sellers.
  • Address: Manages address-related operations.
  • Program: Provides access to programs associated with the API.
  • Account: Contains account-related information and management.
  • Shipment: Deals with shipment-related operations.
  • Invoices: Manages invoice-related data.
  • Labels: Provides endpoints for managing labels.
  • Schedules: Contains scheduling-related endpoints.

You will then debug the Selling Partner API 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 Selling Partner API support.

    dlt init dlthub:selling_partner_migration 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 Selling Partner API API, as specified in @selling_partner_migration-docs.yaml 
Start with endpoints type and  and skip incremental loading for now. 
Place the code in selling_partner_migration_pipeline.py and name the pipeline selling_partner_migration_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 selling_partner_migration_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authorization is based on OAuth2 which includes obtaining an LWA access token that is valid for one hour, and a refresh token that can be used to obtain new access tokens once the original expires. Access to restricted operations requires a Restricted Data Token (RDT).

    To get the appropriate API keys, please visit the original source at https://developer.amazon.com/docs/selling-partner-api/selling-partner-api-overview.html. 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 selling_partner_migration_pipeline.py

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

    Pipeline selling_partner_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset selling_partner_migration_data
The duckdb destination used duckdb:/selling_partner_migration.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 selling_partner_migration_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("selling_partner_migration_pipeline").dataset()
# get yp table as Pandas frame
data.yp.df().head()

Running into errors?

Important considerations include the expiration of the LWA authorization code after five minutes, the need for a refresh token to maintain access, and the specific need for RDTs when calling restricted operations. Additionally, the API has strict rate limits and requires proper IAM permissions and policies.

Extra resources:

Next steps