Buddy-punch Python API Docs | dltHub

Buddy Punch is a time‑tracking platform that provides a private REST API for advanced data automation and custom reporting. The REST API base URL is `` and All requests require a subscription key 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 Buddy-punch data in under 10 minutes.

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What data can I load from Buddy-punch?

Here are some of the endpoints you can load from Buddy-punch:

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How do I authenticate with the Buddy-punch API?

Include the subscription key in each request header, typically as the 'Ocp-Apim-Subscription-Key' header. The key is obtained from the Buddy Punch Developer Portal under Profile → Subscriptions.

1. Get your credentials

1. Visit https://developers.buddypunch.com/ and sign in with an Administrator account.
2. Click Subscribe and provide a name for the subscription.
3. Wait for the Buddy Punch team to approve the request (you will receive an email confirmation).
4. After approval, open the Profile menu in the top‑right corner and select Subscriptions.
5. Copy the displayed subscription key; this is the credential to use in API requests.

2. Add them to .dlt/secrets.toml

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[sources.buddy_punch_source]
subscription_key = "your_subscription_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 Buddy-punch 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 buddy_punch_pipeline.py

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

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Pipeline buddy_punch_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset buddy_punch_data
The duckdb destination used duckdb:/buddy_punch.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 buddy_punch_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 employee and punch from the Buddy-punch 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 buddy_punch_source(subscription_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "subscription_key": subscription_key,
            },
        },
        "resources": [
            {"name": "employee", "endpoint": {"path": "employee"}},
            {"name": "punch", "endpoint": {"path": "punch"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="buddy_punch_pipeline",
        destination="duckdb",
        dataset_name="buddy_punch_data",
    )
    load_info = pipeline.run(buddy_punch_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("buddy_punch_pipeline").dataset()
sessions_df = data.employee.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM buddy_punch_data.employee LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("buddy_punch_pipeline").dataset()
data.employee.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 Buddy-punch 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

• Cause: Missing or invalid subscription key.
• Symptoms: HTTP 401 Unauthorized response.
• Resolution: Verify that the subscription key is correct and included in the Ocp-Apim-Subscription-Key header. Obtain a new key from the Developer Portal if necessary.

Rate limiting

• Cause: Exceeding the allowed number of requests per minute.
• Symptoms: HTTP 429 Too Many Requests response.
• Resolution: Implement exponential back‑off and respect the Retry-After header returned by the API.

Pagination

• Cause: Large result sets returned by list endpoints.
• Symptoms: Responses contain nextLink or similar pagination tokens.
• Resolution: Follow the pagination token in subsequent requests until all pages are retrieved.

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