Load Rezdy data to DuckDB

Rezdy is a booking and distribution platform providing REST APIs for agents, suppliers, and integrations to manage products, availability, and bookings. Everything needed to build a working Rezdy → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Rezdy to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Rezdy to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Rezdy API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Rezdy API at a glance

These values come from the Rezdy API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Rezdy API?

Authentication is performed using an apiKey passed either as a query parameter (?apiKey=...) or as an HTTP header (apiKey: ...), or via OAuth2 bearer tokens in the Authorization header for RezdyConnect (Authorization: Bearer access_token).

1. Get your credentials

To obtain your API credentials, log in to your Rezdy Booking Software dashboard and navigate to 'Integrations' > 'Rezdy API'. If you do not see the 'Request API Key' option, you must submit a request to Rezdy support (support@rezdy.com). Note that API key generation can take up to 48 hours. Once generated, the key will be available on that same 'Rezdy API' page.

2. Add them to .dlt/secrets.toml

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[sources.rezdy_source]
api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Rezdy data can I load into DuckDB?

These are the Rezdy endpoints dlt can load into DuckDB:

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How do I load only new Rezdy records?

Rezdy exposes updated_at on v1/bookings, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "bookings", "endpoint": {
    "path": "v1/bookings",
    "data_selector": "bookings",
    "incremental": {"cursor_path": "updated_at", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Rezdy pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /products and /bookings from the Rezdy API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def rezdy_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.rezdy.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "bookings", "endpoint": {"path": "v1/bookings", "data_selector": "bookings"}},
            {"name": "products", "endpoint": {"path": "v1/products", "data_selector": "products"}}
        ],
    }
    yield from rest_api_resources(config)


def load_rezdy_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rezdy_pipeline",
        destination="duckdb",
        dataset_name="rezdy_data",
    )
    load_info = pipeline.run(rezdy_source())
    print(load_info)


if __name__ == "__main__":
    load_rezdy_to_duckdb()

Run it with python rezdy_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Rezdy data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("rezdy_pipeline").dataset()
df = data.bookings.df()
print(df.head())

SQL:

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SELECT * FROM rezdy_data.bookings LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Rezdy to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Rezdy loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Rezdy data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack