Cloudbeds Python API Docs | dltHub

Cloudbeds is a hospitality management platform that provides a REST API for accessing property data, reservations, rooms, and more. The REST API base URL is https://hotels.cloudbeds.com/api/v1.2 and Requests are authenticated using a long‑lived API key passed as a Bearer token or x‑api‑key header..

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 Cloudbeds data in under 10 minutes.

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What data can I load from Cloudbeds?

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

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How do I authenticate with the Cloudbeds API?

Authentication uses an API key. Include it as Authorization: Bearer <api_key> or as the x-api-key header on every request.

1. Get your credentials

1. Open https://signin.cloudbeds.com/ and log in with an administrator account.
2. Navigate to Account → Apps & Marketplace → API Credentials.
3. Click + New Credentials.
4. Provide a name for the credential and, if required, a redirect URI.
5. Save to generate a Client ID and Client Secret.
6. In the same area, create an API Key for the desired scopes.
7. Copy the displayed API Key (it is shown only once) and store it securely.
8. Optionally, exchange an authorization_code for an API key via the /access_token endpoint using grant_type=urn:ietf:params:oauth:grant-type:api-key.

2. Add them to .dlt/secrets.toml

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[sources.cloudbeds_source]
api_key = "your_api_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 Cloudbeds 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 cloudbeds_pipeline.py

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

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Pipeline cloudbeds_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cloudbeds_data
The duckdb destination used duckdb:/cloudbeds.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 cloudbeds_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 hotels and reservations from the Cloudbeds 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 cloudbeds_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://hotels.cloudbeds.com/api/v1.2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "hotels", "endpoint": {"path": "getHotels"}},
            {"name": "reservations", "endpoint": {"path": "getReservations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cloudbeds_pipeline",
        destination="duckdb",
        dataset_name="cloudbeds_data",
    )
    load_info = pipeline.run(cloudbeds_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("cloudbeds_pipeline").dataset()
sessions_df = data.reservations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cloudbeds_data.reservations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cloudbeds_pipeline").dataset()
data.reservations.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 Cloudbeds 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 Errors

• 401 Unauthorized – occurs when the API key is missing, malformed, or expired. Ensure the Authorization: Bearer <api_key> header (or x-api-key) is present and the key is active.
• 403 Forbidden – indicates the API key does not have the required scopes for the requested method.

Rate Limiting

• 429 Too Many Requests – Cloudbeds enforces 5 requests/second per ClientID for Properties/Associations and 10 requests/second for Tech Partners. If limits are exceeded, the API returns a 429 response; back‑off and retry after a short pause.

Pagination & Data Volume

• Some list endpoints paginate using page and pageSize query parameters. Verify the response includes totalPages or similar fields and iterate until all pages are retrieved.

Request/Response Format

• Requests must be HTTPS and use GET or POST (form‑encoded). Responses are JSON. Missing required query parameters will result in a 400 Bad Request with an error message.

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