Yes.pl Python API Docs | dltHub

Yes.pl is a REST API for the YES loyalty/club platform exposing customer, card, order and product endpoints. The REST API base URL is https://app.club.yes.pl/api and All requests require tokens: newer endpoints use Bearer Authorization; older v1/v2 endpoints accept an access_token path 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 Yes.pl data in under 10 minutes.

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What data can I load from Yes.pl?

Here are some of the endpoints you can load from Yes.pl:

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How do I authenticate with the Yes.pl API?

The API supports Bearer token authentication via the Authorization header (Authorization: Bearer ). Legacy endpoints accept an access_token path parameter (e.g. /v1/{access_token}/...).

1. Get your credentials

1. Log in to your YES (yes.pl) admin/partner dashboard. 2) Locate the API or Integrations / API Tokens section (or contact support@cloudsailor.com if not visible). 3) Create a new token or copy an existing one. 4) Use the token as a Bearer token for v3 endpoints; for legacy endpoints include it as the {access_token} path parameter.

2. Add them to .dlt/secrets.toml

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[sources.yes_pl_source]
access_token = "your_access_token_here"
auth_token = "your_bearer_token_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 Yes.pl 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 yes_pl_pipeline.py

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

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Pipeline yes_pl_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset yes_pl_data
The duckdb destination used duckdb:/yes_pl.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 yes_pl_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 people and customers_by_date from the Yes.pl 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 yes_pl_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.club.yes.pl/api",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "people", "endpoint": {"path": "people"}},
            {"name": "customers_by_date", "endpoint": {"path": "v1/{access_token}/people_by", "data_selector": "customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="yes_pl_pipeline",
        destination="duckdb",
        dataset_name="yes_pl_data",
    )
    load_info = pipeline.run(yes_pl_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("yes_pl_pipeline").dataset()
sessions_df = data.people.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM yes_pl_data.people LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("yes_pl_pipeline").dataset()
data.people.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 Yes.pl 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 403 Forbidden, verify that the Authorization header is set to "Bearer " for v3 endpoints and that the {access_token} path parameter is correct for legacy endpoints. Expired or missing tokens will trigger these responses.

Rate limits and throttling

The public documentation does not specify strict rate limits. If a 429 Too Many Requests response is returned, implement exponential back‑off and respect any Retry‑After header.

Pagination and large result sets

v3 endpoints typically return a top‑level array. Legacy endpoints wrap results in a "customers" (or similar) array; use that key as the data selector and apply query parameters (date, email, etc.) to page through large datasets.

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