Amazon App Submission Python API Docs | dltHub

Amazon App Submission API is a RESTful API to create and manage new versions of Android apps on the Amazon Appstore. The REST API base URL is https://developer.amazon.com/api/appstore/v1 and All requests require a Bearer (Login with Amazon) access token in the Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Amazon App Submission data in under 10 minutes.

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What data can I load from Amazon App Submission?

Here are some of the endpoints you can load from Amazon App Submission:

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How do I authenticate with the Amazon App Submission API?

Obtain a Login with Amazon (LWA) access token via the OAuth2 client_credentials flow against https://api.amazon.com/auth/o2/token using your client_id, client_secret, and scope appstore::apps:readwrite. Include the token in the Authorization header as 'Bearer <access_token>'.

1. Get your credentials

1. In the Amazon Developer Console go to Tools & Services → API Access.
2. Create a new security profile; note the generated client ID and client secret.
3. Associate the security profile with the App Submission API.
4. Request an LWA token by POSTing to https://api.amazon.com/auth/o2/token with grant_type=client_credentials, client_id, client_secret and scope=appstore::apps:readwrite.
5. Use the returned access_token in your API requests.

2. Add them to .dlt/secrets.toml

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[sources.amazon_app_submission_source]
client_id = "amzn1.application-oa2-client.YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Amazon App Submission API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python amazon_app_submission_pipeline.py

If everything is configured correctly, you'll see output like this:

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

Inspect your pipeline and data:

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dlt pipeline amazon_app_submission_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads edits and apks from the Amazon App Submission API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def amazon_app_submission_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://developer.amazon.com/api/appstore/v1",
            "auth": {
                "type": "bearer",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "edits", "endpoint": {"path": "applications/{appId}/edits"}},
            {"name": "apks", "endpoint": {"path": "applications/{appId}/edits/{editId}/apks"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="amazon_app_submission_pipeline",
        destination="duckdb",
        dataset_name="amazon_app_submission_data",
    )
    load_info = pipeline.run(amazon_app_submission_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("amazon_app_submission_pipeline").dataset()
sessions_df = data.edits.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM amazon_app_submission_data.edits LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("amazon_app_submission_pipeline").dataset()
data.edits.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Amazon App Submission data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authorization failures

If you receive {"message":"Request is not authorized."} or a 403 response, obtain a fresh LWA access token via POST https://api.amazon.com/auth/O2/token with client_credentials and scope appstore::apps:readwrite. Ensure the token is included as Authorization: Bearer <access_token>.

API version and App ID errors

A 400 error with "API Version not supported" indicates an incorrect base path; use the v1 base URL. A 404 error with "No app found" means the appId in the path is wrong.

ETag concurrency and required headers

PUT/DELETE operations often require the current ETag value. Retrieve it from the ETag response header of a preceding GET and send it as If-Match: <ETag>.

Rate limits and retry

The API does not publish a fixed limit. Treat 429 or transient 5xx responses as retryable with exponential backoff.

Pagination and list handling

List endpoints return either a top‑level array (e.g., /apks) or a named array such as images or amazonDevices. There is no generic pagination wrapper, so iterate over the array directly.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack