Tremendous Python API Docs | dltHub

Tremendous is a platform for sending rewards, incentives, and payouts globally via a REST API. The REST API base URL is https://api.tremendous.com/api/v2 (production) — sandbox: https://testflight.tremendous.com/api/v2 and All requests require a Bearer token (API key) or OAuth access token for authentication..

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 Tremendous data in under 10 minutes.

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What data can I load from Tremendous?

Here are some of the endpoints you can load from Tremendous:

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How do I authenticate with the Tremendous API?

Use an API key as a Bearer token in the Authorization header (Authorization: Bearer YOUR-API-KEY). For Connect integrations use OAuth client_id/secret to obtain an access token and then use that token as a Bearer token.

1. Get your credentials

1. Sign in to your Tremendous sandbox account. 2) In Team settings → Developers, click Register app to create a developer app and obtain CLIENT_ID and CLIENT_SECRET (for OAuth) or create an API key for sandbox. 3) For production, complete the Production API Access form and required company documentation, then create a production API key in your account. 4) Use the returned API key as the Bearer token in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.tremendous_source]
api_key = "YOUR_TREMENDOUS_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 Tremendous 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 tremendous_pipeline.py

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

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Pipeline tremendous_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tremendous_data
The duckdb destination used duckdb:/tremendous.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 tremendous_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 rewards from the Tremendous 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 tremendous_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tremendous.com/api/v2 (production) — sandbox: https://testflight.tremendous.com/api/v2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "api/v2/orders", "data_selector": "orders"}},
            {"name": "rewards", "endpoint": {"path": "api/v2/rewards", "data_selector": "rewards"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM tremendous_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tremendous_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 Tremendous 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, verify the Authorization header is set to: Authorization: Bearer YOUR-API-KEY (or OAuth access token). Ensure OAuth flows have exchanged the code for an access token.

Production access / onboarding

To use production you must apply for production API access and submit company documentation; production requests must be directed to https://api.tremendous.com and use your production API key.

Rate limiting and errors

The API returns standard HTTP errors (401 Unauthorized, 403 Forbidden for insufficient permissions, 422 Unprocessable Entity for validation errors). Inspect the JSON error body returned with the status code for detailed messages.

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