Digital river Python API Docs | dltHub

Digital River is a commerce platform and REST API that provides payments, order management, subscription and product management, tax and fraud services for online sellers. The REST API base URL is https://api.digitalriver.com and all requests require a secret API key sent as a Bearer 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 Digital river data in under 10 minutes.

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What data can I load from Digital river?

Here are some of the endpoints you can load from Digital river:

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How do I authenticate with the Digital river API?

Use your account secret API key (test keys begin with sk_test_, production with sk_) and include it in each request as Authorization: Bearer <SECRET_KEY>. Also set Content-Type: application/json. Optionally include DigitalRiver-Version header to override API version.

1. Get your credentials

1. Sign in to the Digital River Dashboard (or contact your Account Manager if required).
2. Open Developers / API Keys (or API credentials) section in the Dashboard.
3. Create or view secret keys; test keys use prefix sk_test_ and production keys use sk_.
4. Copy the secret key and store it securely (do not commit to source control). Use this key as the Bearer token.

2. Add them to .dlt/secrets.toml

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[sources.digital_river_source]
# put the secret key value here
token = "sk_test_your_secret_key_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 Digital river 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 digital_river_pipeline.py

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

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Pipeline digital_river_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset digital_river_data
The duckdb destination used duckdb:/digital_river.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 digital_river_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 customers from the Digital river 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 digital_river_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.digitalriver.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "data"}},
            {"name": "customers", "endpoint": {"path": "customers", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM digital_river_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("digital_river_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 Digital river 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 you receive 401 Unauthorized or errors with code api_key_expired or unauthorized: verify you are using the correct secret key (sk_test_ vs sk_), ensure it is sent as Authorization: Bearer , and that the key has the required permissions (Admin APIs require keys with admin access). Check x-dr-requestid in response headers and API logs in Dashboard for details.

Rate limiting (too_many_requests)

If requests return 429 Too Many Requests or error type too_many_requests, respect Retry-After header when provided and implement exponential backoff. Digital River may return rate info in response headers; throttle client accordingly.

Pagination quirks

List endpoints use cursor-based pagination (parameters: limit, startingAfter, endingBefore). List responses are returned as an object with a data array and hasMore flag. Use the object IDs from data elements as cursors for startingAfter/endingBefore. Limit allowed between 1 and 100.

Common API errors

Digital River documents many error codes (bad_request, not_found, api_key_expired, validation_error, conflict, internal_server_error, payment_authorization_failed, out_of_inventory). Error responses include type and errors[] with code and message—log both message and code for diagnostics but avoid exposing codes to end-users.

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