Booking.com Python API Docs | dltHub

Booking.com Connectivity API is used to process property reservations and manage property-related information through various XML and JSON endpoints. The REST API base URL is https://supply-xml.booking.com (for non-PCI endpoints) and https://secure-supply-xml.booking.com (for PCI/reservations endpoints). and All requests require HTTP Basic authentication using machine account credentials (username and password)..

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 Booking.com data in under 10 minutes.

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What data can I load from Booking.com?

Here are some of the endpoints you can load from Booking.com:

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How do I authenticate with the Booking.com API?

Authentication uses HTTP Basic authentication. Credentials (username: machine account ID, password: secret) are base64 encoded and sent in the Authorization header as Basic <base64 credentials>. Requests must use HTTPS and include the correct Host header.

1. Get your credentials

Please refer to the Booking.com Connectivity Partner Portal or contact your Booking.com account manager to obtain machine account credentials (username and secret).

2. Add them to .dlt/secrets.toml

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[sources.booking_com_source]
username = "your_machine_account_id"
password = "your_secret"

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 Booking.com 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 booking_com_pipeline.py

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

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Pipeline booking_com_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset booking_com_data
The duckdb destination used duckdb:/booking_com.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 booking_com_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 reservations_summary and ota_hotel_res_notif from the Booking.com 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 booking_com_source(username, password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://supply-xml.booking.com (for non-PCI endpoints) and https://secure-supply-xml.booking.com (for PCI/reservations endpoints).",
            "auth": {
                "type": "http_basic",
                "username, password": username, password,
            },
        },
        "resources": [
            {"name": "reservations_summary", "endpoint": {"path": "reservationssummary", "data_selector": "reservationsummary/reservationSummary"}},
            {"name": "ota_hotel_res_notif", "endpoint": {"path": "OTA_HotelResNotif", "data_selector": "HotelReservations/HotelReservation"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="booking_com_pipeline",
        destination="duckdb",
        dataset_name="booking_com_data",
    )
    load_info = pipeline.run(booking_com_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("booking_com_pipeline").dataset()
sessions_df = data.reservations_summary.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM booking_com_data.reservations_summary LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("booking_com_pipeline").dataset()
data.reservations_summary.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 Booking.com 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

Authentication failures (401 Unauthorized) can occur due to incorrect machine account credentials (username or secret) or an improperly formatted Authorization header. Ensure the username and password are correct and base64 encoded as Basic <base64 credentials>.

Invalid Requests

Requests may return a 400 Bad Request error if the XML or JSON body is malformed or does not adhere to the expected schema. Verify the request body's structure and content. Incorrect Content-Type headers can also lead to this error; ensure it matches the data format being sent (e.g., application/xml or application/json).

Forbidden Access

A 403 Forbidden error indicates that the provided credentials lack the necessary permissions to access the requested resource. Review your machine account's permissions in the Booking.com Connectivity Partner Portal.

Rate Limiting

Requests may be throttled, resulting in a 429 Too Many Requests error, if the rate limits are exceeded. Consult the Booking.com Connectivity Partner Portal for specific rate limit details and implement appropriate retry mechanisms with exponential backoff.

Host Header Mismatch

Ensure that the Host header in your request correctly matches the base URL being used (e.g., supply-xml.booking.com or secure-supply-xml.booking.com). Mismatched host headers can lead to connection issues or incorrect routing.

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