Prefinery Python API Docs | dltHub

Prefinery is a platform for managing product betas, user signups, referrals and reward/points systems for pre‑launch campaigns. The REST API base URL is https://api.prefinery.com/api/v2 and All requests require your Prefinery API key (supports HTTP Basic, Bearer header, or URL parameter)..

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 Prefinery data in under 10 minutes.

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What data can I load from Prefinery?

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

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How do I authenticate with the Prefinery API?

Prefinery uses token‑based authentication via your API key. You can authenticate via HTTP Basic (API key as username, blank password), an Authorization: Bearer <API_KEY> header, or by passing ?api_key=<API_KEY> in the URL.

1. Get your credentials

1. Sign in to your Prefinery account.
2. Open the settings menu (upper‑right) and select Company Settings.
3. Choose API Keys (or API Access) and create a new API key.
4. Copy and securely store the generated secret key.

2. Add them to .dlt/secrets.toml

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[sources.prefinery_source]
api_key = "your_prefinery_api_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 Prefinery 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 prefinery_pipeline.py

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

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Pipeline prefinery_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset prefinery_data
The duckdb destination used duckdb:/prefinery.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 prefinery_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 betas and testers from the Prefinery 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 prefinery_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.prefinery.com/api/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "betas", "endpoint": {"path": "betas", "data_selector": "betas"}},
            {"name": "testers", "endpoint": {"path": "testers", "data_selector": "testers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="prefinery_pipeline",
        destination="duckdb",
        dataset_name="prefinery_data",
    )
    load_info = pipeline.run(prefinery_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("prefinery_pipeline").dataset()
sessions_df = data.betas.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM prefinery_data.betas LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("prefinery_pipeline").dataset()
data.betas.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 Prefinery 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 ensure your API key is correct and is sent via Basic auth username (empty password), Authorization: Bearer <API_KEY> header, or ?api_key=<API_KEY> query parameter. 403 Forbidden indicates API access not enabled or insufficient permissions.

Rate limiting / 503 Service Unavailable

When rate limiting is triggered the API returns HTTP 503. There is also a limit of 256 simultaneous connections per IP.

Pagination

List endpoints are paginated. Use the ?page parameter. Pagination metadata is returned in the X-Pagination response header as a JSON object (previous_page, next_page, current_page, per_page, count, pages, total_count).

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