Viator Python API Docs | dltHub

The Viator Supplier API allows partners to access product data, pricing, and availability for real-time and bulk operations. To connect, sign up with Viator and access the supplier management center. The API supports real-time availability checks and booking requests. The REST API base URL is https://api.viator.com/partner (production); sandbox: https://api.sandbox.viator.com/partner and All requests require an API key supplied in a request 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 Viator data in under 10 minutes.

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What data can I load from Viator?

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

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How do I authenticate with the Viator API?

Access is managed using an API key included as a header parameter on every request (header name example: exp-api-key). All endpoints use HTTPS.

1. Get your credentials

Log into the Viator Partner portal, navigate to the API or Integration settings section, and locate the Operator API key. If an API key is not visible, request one from partner support via the provided email address (affiliateapi@viator.com). The key is assigned per Operator account.

2. Add them to .dlt/secrets.toml

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[sources.viator_reservation_system_api_source]
api_key = "your_viator_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 Viator 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 viator_reservation_system_api_pipeline.py

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

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Pipeline viator_reservation_system_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset viator_reservation_system_api_data
The duckdb destination used duckdb:/viator_reservation_system_api.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 viator_reservation_system_api_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 products and availability_schedules from the Viator 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 viator_reservation_system_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.viator.com/partner (production); sandbox: https://api.sandbox.viator.com/partner",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products/modified-since"}},
            {"name": "availability_schedules", "endpoint": {"path": "availability/schedules/{product-code}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="viator_reservation_system_api_pipeline",
        destination="duckdb",
        dataset_name="viator_reservation_system_api_data",
    )
    load_info = pipeline.run(viator_reservation_system_api_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("viator_reservation_system_api_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM viator_reservation_system_api_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("viator_reservation_system_api_pipeline").dataset()
data.products.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 Viator 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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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