Load PyVista data to DuckDB

PyVista is a Python library for 3D plotting and mesh analysis that provides a Pythonic interface to VTK mesh data types. Everything needed to build a working PyVista → 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 PyVista 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 PyVista 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 PyVista 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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PyVista API at a glance

These values come from the PyVista API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the PyVista API?

PyVista does not provide a public REST API; it is a Python library for 3D visualization and mesh analysis. Consequently, there is no authentication mechanism or required header.

No credentials required. The PyVista API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.

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What PyVista data can I load into DuckDB?

These are the PyVista endpoints dlt can load into DuckDB:

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How do I load only new PyVista records?

The PyVista 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": "records", "endpoint": {
    "path": "records",
    # 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 PyVista pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading none and none from the PyVista API into DuckDB:

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

@dlt.source
def pyvista_source():
    config: RESTAPIConfig = {
        "client": {
            "base_url": "none",
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def load_pyvista_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pyvista_pipeline",
        destination="duckdb",
        dataset_name="pyvista_data",
    )
    load_info = pipeline.run(pyvista_source())
    print(load_info)


if __name__ == "__main__":
    load_pyvista_to_duckdb()

Run it with python pyvista_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 PyVista 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("pyvista_pipeline").dataset()
df = data.none.df()
print(df.head())

SQL:

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SELECT * FROM pyvista_data.none LIMIT 10;

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

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How do I deploy the PyVista 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 PyVista 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 PyVista 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