Order-desk Python API Docs | dltHub

Order Desk is a RESTful service that provides programmatic access to an ecommerce store’s orders, shipments, inventory items, store settings, and related resources. The REST API base URL is https://app.orderdesk.me/api/v2 and all requests require two header credentials: ORDERDESK-STORE-ID and ORDERDESK-API-KEY.

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 Order-desk data in under 10 minutes.

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What data can I load from Order-desk?

Here are some of the endpoints you can load from Order-desk:

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How do I authenticate with the Order-desk API?

Order Desk uses an API key + store ID header‑based authentication. Every request must include the headers ORDERDESK-STORE-ID: and ORDERDESK-API-KEY: and use JSON content‑type when sending bodies.

1. Get your credentials

Go to Order Desk > Store Settings (API tab) / Settings > Advanced -> API tab. Click “Create API Key”. The UI will display an API Key and Store ID. Record both values and use them as the ORDERDESK-API-KEY and ORDERDESK-STORE-ID headers in each request.

2. Add them to .dlt/secrets.toml

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[sources.order_desk_source]
ORDERDESK_STORE_ID = "your_store_id"
ORDERDESK_API_KEY = "your_api_key"

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 Order-desk 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 order_desk_pipeline.py

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

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Pipeline order_desk_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset order_desk_data
The duckdb destination used duckdb:/order_desk.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 order_desk_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 orders and inventory_items from the Order-desk 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 order_desk_source(orderdesk_store_id, orderdesk_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.orderdesk.me/api/v2",
            "auth": {
                "type": "api_key",
                "ORDERDESK_API_KEY (and ORDERDESK_STORE_ID as separate header)": orderdesk_store_id, orderdesk_api_key,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "orders"}},
            {"name": "inventory_items", "endpoint": {"path": "inventory-items", "data_selector": "inventory_items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="order_desk_pipeline",
        destination="duckdb",
        dataset_name="order_desk_data",
    )
    load_info = pipeline.run(order_desk_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("order_desk_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM order_desk_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("order_desk_pipeline").dataset()
data.orders.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 Order-desk 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 ORDERDESK-API-KEY or ORDERDESK-STORE-ID are missing or invalid, the API returns an error status and message. Ensure both headers are present and contain the correct values.

Rate limiting

The API enforces rate limits. When exceeded, it responds with HTTP 429 and a JSON body like:

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{ "status": "error", "message": "API Rate Limit Exceeded" }

Response headers X-Tokens-Available and X-Retry-After indicate when to resume requests. Implement exponential back‑off and respect Retry-After.

Pagination

List endpoints accept limit (default 50, max 500) and offset parameters. Responses include total_records, records_returned, offset, and limit. Use these fields to iterate through pages until records_returned is less than limit.

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