Ezus Python API Docs | dltHub

Ezus API follows REST principles, enabling travel agencies to automate quotes and manage itineraries. It integrates with various tools for operational efficiency. The API is designed for DMCs and travel planners. The REST API base URL is https://api.ezus.app and All requests require an API key (x-api-key header) or a Bearer token (Authorization 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 Ezus data in under 10 minutes.

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What data can I load from Ezus?

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

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How do I authenticate with the Ezus API?

Include your API key in the x-api-key header for most requests, or obtain a Bearer token via the /login endpoint and pass it in the Authorization header.

1. Get your credentials

1. Log in to your Ezus account at https://ezus.io.
2. Navigate to the "API Settings" or "Integrations" section.
3. Generate a new API key and copy its value.
4. To obtain a Bearer token, send a POST request to https://api.ezus.app/login with your username and password in the request body.
5. The response will contain a token field; copy this token for use in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.ezus_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 Ezus 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 ezus_pipeline.py

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

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Pipeline ezus_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ezus_data
The duckdb destination used duckdb:/ezus.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 ezus_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 projects and products from the Ezus 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 ezus_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.ezus.app",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "projects"}},
            {"name": "products", "endpoint": {"path": "products", "data_selector": "products"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ezus_pipeline",
        destination="duckdb",
        dataset_name="ezus_data",
    )
    load_info = pipeline.run(ezus_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("ezus_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ezus_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ezus_pipeline").dataset()
data.projects.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 Ezus 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