Load Xarray data to DuckDB

Xpublish is a library that allows publishing Xarray Datasets or DataTrees via a REST API based on FastAPI. Everything needed to build a working Xarray → 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 Xarray 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 Xarray 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 Xarray 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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Xarray API at a glance

These values come from the Xarray API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Xarray API?

Xpublish (the REST API library for Xarray) provides a framework built on FastAPI; it does not implement built-in authentication by default, so any authentication layer must be configured manually by the user or via custom FastAPI plugins or middleware.

1. Get your credentials

Xpublish does not provide a managed dashboard or built-in authentication system. It is a library used to build custom FastAPI applications for serving Xarray Datasets. To implement authentication (such as API keys), you must add FastAPI security dependencies (e.g., OAuth2 with API key support) to your Xpublish instance using the plugin system or by customizing the FastAPI app instance directly via rest.app.

2. Add them to .dlt/secrets.toml

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[sources.xarray_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 Xarray data can I load into DuckDB?

These are the Xarray endpoints dlt can load into DuckDB:

| Resource | Endpoint | Method | Data selector | Description |\n| --- | --- | --- | --- | --- |\n| datasets | /datasets | GET | | Returns a list of all dataset IDs. |\n| dataset_info | /datasets/{dataset_id}/ | GET | | Returns the HTML representation of the dataset. |\n| dataset_keys | /datasets/{dataset_id}/keys | GET | | Returns a list of variable keys in the dataset. |\n| dataset_dict | /datasets/{dataset_id}/dict | GET | | Returns a JSON dictionary of the full dataset. |\n| dataset_meta | /datasets/{dataset_id}/info | GET | | Returns a JSON dictionary summary of dataset variables and attributes. |\n| versions | /versions | GET | | Returns a JSON dictionary of package versions on the server. | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

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How do I load only new Xarray records?

The Xarray API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "datasets", "endpoint": {
    "path": "datasets",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Xarray pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /info and /zarr/.zmetadata from the Xarray API into DuckDB:

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

@dlt.source
def xarray_source(not_applicable=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "user_defined (the API is served locally at a user-specified host and port, e.g., http://0.0.0.0:9000)",
            "auth": {"type": "api_key", "api_key": not_applicable, "name": "not_applicable", "location": "header"},
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "datasets"}},
            {"name": "dataset_info", "endpoint": {"path": "datasets/{dataset_id}/info"}}
        ],
    }
    yield from rest_api_resources(config)


def load_xarray_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="xarray_pipeline",
        destination="duckdb",
        dataset_name="xarray_data",
    )
    load_info = pipeline.run(xarray_source())
    print(load_info)


if __name__ == "__main__":
    load_xarray_to_duckdb()

Run it with python xarray_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 Xarray 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("xarray_pipeline").dataset()
df = data.dataset_info.df()
print(df.head())

SQL:

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SELECT * FROM xarray_data.dataset_info LIMIT 10;

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

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How do I deploy the Xarray 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 Xarray 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 Xarray 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