Windborne Systems Python API Docs | dltHub

Windborne Systems API is a REST service delivering weather forecast data, including point, gridded, and tropical cyclone forecasts. The REST API base URL is https://forecasts.windbornesystems.com/api/v1 and All requests require HTTP basic authentication with a client ID and API key..

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 Windborne Systems data in under 10 minutes.

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What data can I load from Windborne Systems?

Here are some of the endpoints you can load from Windborne Systems:

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How do I authenticate with the Windborne Systems API?

Authentication is performed via HTTP Basic Auth where the username is the client ID and the password is the API key, sent in the Authorization header of each request.

1. Get your credentials

1. Open https://app.windbornesystems.com/api_tokens in a web browser.
2. Log in with your Windborne Systems account credentials.
3. Click "Create New API Token".
4. Copy the generated Client ID and API Key.
5. Store them securely for use in dlt configuration.

2. Add them to .dlt/secrets.toml

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[sources.windborne_systems_source]
client_id = "your_client_id_here"
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 Windborne Systems 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 windborne_systems_pipeline.py

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

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Pipeline windborne_systems_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset windborne_systems_data
The duckdb destination used duckdb:/windborne_systems.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 windborne_systems_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 points and initialization_times from the Windborne Systems 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 windborne_systems_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://forecasts.windbornesystems.com/api/v1",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "points", "endpoint": {"path": "points.json", "data_selector": "forecasts"}},
            {"name": "initialization_times", "endpoint": {"path": "initialization_times.json", "data_selector": "available"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="windborne_systems_pipeline",
        destination="duckdb",
        dataset_name="windborne_systems_data",
    )
    load_info = pipeline.run(windborne_systems_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("windborne_systems_pipeline").dataset()
sessions_df = data.points.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM windborne_systems_data.points LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("windborne_systems_pipeline").dataset()
data.points.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 Windborne Systems 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 – Returned when the client ID or API key is missing or invalid. Ensure HTTP Basic Auth header contains the correct credentials.

Missing Data Errors

• 404 Not Found – Occurs for requests to historical or unavailable forecast endpoints (e.g., requesting a gridded dataset that does not exist for the requested time).

Rate Limiting

• 429 Too Many Requests – The API may enforce request limits per minute/hour. If received, back off and retry after a short delay.

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