PartnerStack Python API Docs | dltHub

PartnerStack is a platform that provides APIs for managing partner programs, including rewards, transactions, and customer relationships. The REST API base URL is https://api.partnerstack.com/api/v2 and The Partner API uses Bearer Token authentication, while the Vendor API uses HTTP Basic 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 PartnerStack data in under 10 minutes.

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What data can I load from PartnerStack?

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

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How do I authenticate with the PartnerStack API?

For the Partner API, authenticate requests using Bearer Auth by setting your API key as the bearer token in the Authorization header. For the Vendor API, use HTTP Basic Auth with your public key as the username and private key as the password.

1. Get your credentials

API keys (both Test and Production) can be found in your integration settings within your PartnerStack account.

2. Add them to .dlt/secrets.toml

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[sources.partner_stack_source]
api_key = "your_partner_api_key_here"
public_key = "your_vendor_public_key_here"
private_key = "your_vendor_private_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 PartnerStack 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 partner_stack_pipeline.py

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

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Pipeline partner_stack_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset partner_stack_data
The duckdb destination used duckdb:/partner_stack.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 partner_stack_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 rewards and marketplace_programs from the PartnerStack 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 partner_stack_source(api_key, public_key, private_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.partnerstack.com/api/v2",
            "auth": {
                "type": "bearer, http_basic",
                "api_key, public_key, private_key": api_key, public_key, private_key,
            },
        },
        "resources": [
            {"name": "rewards", "endpoint": {"path": "rewards", "data_selector": "data.items"}},
            {"name": "marketplace_programs", "endpoint": {"path": "marketplace/programs", "data_selector": "data.items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="partner_stack_pipeline",
        destination="duckdb",
        dataset_name="partner_stack_data",
    )
    load_info = pipeline.run(partner_stack_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("partner_stack_pipeline").dataset()
sessions_df = data.rewards.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM partner_stack_data.rewards LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("partner_stack_pipeline").dataset()
data.rewards.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 PartnerStack 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 your requests are failing due to authentication issues, ensure that for the Partner API, you are using your api_key as a Bearer token in the Authorization header. For the Vendor API, verify that you are providing your public key as the username and private key as the password for HTTP Basic authentication. The API returns JSON error objects with a message and status field for failed requests.

Rate Limits

The PartnerStack API has rate limits in place to prevent abuse. While specific details on the limits are not provided in the reviewed documentation, exceeding these limits will result in error responses. Implement appropriate retry mechanisms and back-off strategies in your integration to handle rate limit errors gracefully.

Pagination Issues

When using paginated endpoints, ensure that starting_after and ending_before parameters are not used together in the same request, as they are mutually exclusive. Incorrect usage of these parameters or the limit parameter can lead to unexpected results or errors. Paginated responses include a data.has_more field to indicate if further pages are available.

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