Load Partnerize data to DuckDB

Partnerize is an affiliate marketing platform that provides APIs for managing and tracking partnerships, campaigns, and conversions for both partners and brands. Everything needed to build a working Partnerize → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Partnerize to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Partnerize to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Partnerize API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Partnerize API at a glance

These values come from the Partnerize API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Partnerize API?

Authentication is achieved via HTTP Basic Auth. You must provide an 'Authorization' header with the value 'Basic ' followed by the base64-encoded string of 'application_key:user_api_key'.

1. Get your credentials

1. Log in to the Partnerize console at https://console.partnerize.com. 2. Navigate to the Settings menu (often found via the Partnerize dropdown). 3. Select Account settings or Your Account. 4. Locate the User Application Key and User API Key displayed on the page.

2. Add them to .dlt/secrets.toml

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[sources.partnerize_source]
application_key = "your_application_key_here"
user_api_key = "your_user_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Partnerize data can I load into DuckDB?

These are the Partnerize endpoints dlt can load into DuckDB:

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How do I load only new Partnerize records?

Partnerize exposes cursor_id on reporting/report_publisher/publisher/{PUBLISHER_ID}/conversion.json, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "publisher_conversions", "endpoint": {
    "path": "reporting/report_publisher/publisher/{PUBLISHER_ID}/conversion.json",
    "incremental": {"cursor_path": "cursor_id", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Partnerize pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v3/brand/ and v3/conversions (or user depending on the specific API implementation) from the Partnerize API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def partnerize_source(application_key_user_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.partnerize.com/v3",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": application_key_user_api_key},
        },
        "resources": [
            {"name": "publisher_conversions", "endpoint": {"path": "reporting/report_publisher/publisher/{PUBLISHER_ID}/conversion.json"}},
            {"name": "partner_groups", "endpoint": {"path": "v3/partner/groups"}}
        ],
    }
    yield from rest_api_resources(config)


def load_partnerize_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="partnerize_pipeline",
        destination="duckdb",
        dataset_name="partnerize_data",
    )
    load_info = pipeline.run(partnerize_source())
    print(load_info)


if __name__ == "__main__":
    load_partnerize_to_duckdb()

Run it with python partnerize_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Partnerize data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("partnerize_pipeline").dataset()
df = data.publisher_conversions.df()
print(df.head())

SQL:

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SELECT * FROM partnerize_data.publisher_conversions LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Partnerize to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Partnerize loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Partnerize data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack