Bookingbug Python API Docs | dltHub

JRNI (formerly Bookingbug) is a scheduling platform and REST API for managing services, available times and bookings. The REST API base URL is https://{host}/api/v5 and Requests require an App‑Id header; admin/member endpoints require Auth‑Token or OAuth2 access token depending on permission..

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 Bookingbug data in under 10 minutes.

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What data can I load from Bookingbug?

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

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How do I authenticate with the Bookingbug API?

Include the App‑Id header on all requests (App-Id: YOUR_APP_ID). For member or admin endpoints also include Auth-Token (Auth-Token: YOUR_TOKEN) or an OAuth2 Bearer token (Authorization: Bearer ).

1. Get your credentials

1. Log in to the JRNI admin dashboard.
2. Navigate to the API or Integration settings section.
3. Create a new API client or locate the existing App Id for your application.
4. Copy the App Id value.
5. For Auth‑Token or OAuth2, follow the "Create API client" flow to generate client credentials and complete the OAuth2 authorization code flow to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.bookingbug_source]
app_id = "your_app_id_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 Bookingbug 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 bookingbug_pipeline.py

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

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Pipeline bookingbug_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bookingbug_data
The duckdb destination used duckdb:/bookingbug.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 bookingbug_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 services and times from the Bookingbug 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 bookingbug_source(app_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{host}/api/v5",
            "auth": {
                "type": "api_key",
                "app_id": app_id,
            },
        },
        "resources": [
            {"name": "services", "endpoint": {"path": "api/v5/{company_id}/services", "data_selector": "_embedded.services"}},
            {"name": "times", "endpoint": {"path": "api/v5/{company_id}/times", "data_selector": "_embedded.times"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bookingbug_pipeline",
        destination="duckdb",
        dataset_name="bookingbug_data",
    )
    load_info = pipeline.run(bookingbug_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("bookingbug_pipeline").dataset()
sessions_df = data.services.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bookingbug_data.services LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bookingbug_pipeline").dataset()
data.services.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 Bookingbug 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 or 403 responses, ensure the App‑Id header is present and correct. For member or admin endpoints also include a valid Auth‑Token or Bearer token with the required scopes.

Pagination and embedded resources

JRNI uses HAL‑style _embedded objects and returns pagination fields such as total_entries, page, and per_page. Iterate through pages using these query parameters.

Rate limits and errors

The API does not publish a strict rate limit; if a 429 response is received, implement exponential back‑off before retrying. Validation errors return a 4xx status with details in the response body.

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