Propeller Commerce Python API Docs | dltHub

Propeller Commerce is a commerce platform providing REST APIs for bulk import/export and management of e-commerce resources (products, orders, inventory, users, etc). The REST API base URL is https://api.helice.cloud and all requests require an OAuth 2.0 access token (client credentials) used as a Bearer token.

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 Propeller Commerce data in under 10 minutes.

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What data can I load from Propeller Commerce?

Here are some of the endpoints you can load from Propeller Commerce:

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How do I authenticate with the Propeller Commerce API?

Obtain an access token via OAuth2 client_credentials by POSTing client_id and client_secret to https://api.helice.cloud/oauth2/token; include the returned access_token in the Authorization header as 'Authorization: Bearer ' on all API requests. Tokens expire after 30 minutes.

1. Get your credentials

1. Sign in to your Propeller/Helice admin or developer portal and create an API client or application. 2) Note the provided client_id and client_secret. 3) Request an access token via POST to https://api.helice.cloud/oauth2/token with client_id, client_secret and grant_type=client_credentials. 4) Use the returned access_token as Bearer token in API requests. 5) Refresh by repeating the token request before expiration.

2. Add them to .dlt/secrets.toml

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[sources.propeller_commerce_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"

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 Propeller Commerce 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 propeller_commerce_pipeline.py

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

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Pipeline propeller_commerce_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset propeller_commerce_data
The duckdb destination used duckdb:/propeller_commerce.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 propeller_commerce_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 products and orders from the Propeller Commerce 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 propeller_commerce_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.helice.cloud",
            "auth": {
                "type": "oauth2",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "v2/products", "data_selector": "data"}},
            {"name": "orders", "endpoint": {"path": "v2/orders", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="propeller_commerce_pipeline",
        destination="duckdb",
        dataset_name="propeller_commerce_data",
    )
    load_info = pipeline.run(propeller_commerce_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("propeller_commerce_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM propeller_commerce_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("propeller_commerce_pipeline").dataset()
data.products.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 Propeller Commerce 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

Authentication failures

If API requests return 401 Unauthorized or authentication errors, request a new access token via POST https://api.helice.cloud/oauth2/token with client_id and client_secret; tokens expire after 30 minutes. Common error codes: invalid_client, invalid_token.

Rate limits and 429 responses

The docs reference rate_limit_exceeded (429). Implement exponential backoff and retries.

Pagination and data selectors

Most GET endpoints are paginated and return the records in the 'data' array; use 'page' and 'offset' query parameters. Some bulk endpoints return resource-specific arrays (e.g. 'usergroups' or 'products'). Ensure you read the endpoint response example to get the exact array key.

Error response format

Errors are returned in an 'error' object with code, status, type, messages and moreInfo. Example: {"error":{"code":80006,"status":404,"type":"ProductNotFound","messages":["Product with id [481189] not found"],"moreInfo":"https://www.propel.us/docs/errors/80006"}}

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