Book-like-a-boss Python API Docs | dltHub

Book Like A Boss is a scheduling platform that lets users create booking pages and integrates with other apps via webhooks and third‑party connectors. The REST API base URL is `` and No direct authentication required; integrations use webhooks or connector‑platform account linking..

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 Book-like-a-boss data in under 10 minutes.

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What data can I load from Book-like-a-boss?

Here are some of the endpoints you can load from Book-like-a-boss:

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How do I authenticate with the Book-like-a-boss API?

1. Get your credentials

1. Log in to your Book Like A Boss account.
2. Navigate to Settings → Integrations → Webhooks.
3. Click “Add Webhook”, enter the target URL, and save.
4. For connector platforms (Make, Zapier, etc.), open the platform, add a new Book Like A Boss connection, and follow the on‑screen account‑linking prompts.

2. Add them to .dlt/secrets.toml

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[sources.book_like_a_boss_source]

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 Book-like-a-boss 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 book_like_a_boss_pipeline.py

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

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Pipeline book_like_a_boss_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset book_like_a_boss_data
The duckdb destination used duckdb:/book_like_a_boss.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 book_like_a_boss_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 from the Book-like-a-boss 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 book_like_a_boss_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="book_like_a_boss_pipeline",
        destination="duckdb",
        dataset_name="book_like_a_boss_data",
    )
    load_info = pipeline.run(book_like_a_boss_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("book_like_a_boss_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM book_like_a_boss_data. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("book_like_a_boss_pipeline").dataset()
data..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 Book-like-a-boss 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

Webhook Delivery Errors

• Ensure the target URL returns an HTTP 200 response within a few seconds.
• Check that the payload format (JSON) matches what your receiving service expects.
• Use the platform's test webhook feature to verify connectivity.

Connector Authentication Issues

• If the Make/Zapier/Pabbly integration cannot connect, re‑authenticate the Book Like A Boss account within the connector's UI.
• Verify that the account has appropriate permissions and that the webhook URL is correctly entered.

Rate Limits / Pagination

• Not applicable; there are no public GET endpoints to rate‑limit or paginate.

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