Load PySTAC data to DuckDB

PySTAC Client is a Python library for working with Spatiotemporal Asset Catalog (STAC) APIs and catalogs conforming to STAC specifications. Everything needed to build a working PySTAC → 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 PySTAC 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 PySTAC 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 PySTAC 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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PySTAC API at a glance

These values come from the PySTAC API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the PySTAC API?

PySTAC Client does not have a single native authentication mechanism; instead, it uses request headers passed to the Client.open method or a request_modifier hook to handle various methods like Basic or Token auth. Headers are typically passed as a dictionary.

1. Get your credentials

PySTAC itself is a library for interacting with STAC APIs and does not have a central dashboard or universal API key system. To obtain credentials for a specific STAC API provider, you must log in to the provider's specific portal (e.g., mlhub.earth for Radiant MLHub). Navigate to the user profile or settings section, typically labeled 'Settings & API Keys' or 'Dashboard', to generate a new API key.

2. Add them to .dlt/secrets.toml

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[sources.pystac_source]
api_key = "your_api_key_here"
# Example implementation depending on API requirement:
# headers = {"Authorization": "Bearer your_api_key_here"}
# parameters = {"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 PySTAC data can I load into DuckDB?

These are the PySTAC endpoints dlt can load into DuckDB:

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How do I load only new PySTAC records?

PySTAC exposes links (rel=next) on search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "items", "endpoint": {
    "path": "search",
    "data_selector": "features",
    "incremental": {"cursor_path": "links (rel=next)", "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 PySTAC pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading open and search from the PySTAC API into DuckDB:

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

@dlt.source
def pystac_source(headers=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Not applicable; PySTAC is a client library for STAC APIs, so the base URL depends on the specific STAC API instance being accessed.",
            "auth": {"type": "bearer", "token": headers},
        },
        "resources": [
            {"name": "items", "endpoint": {"path": "search", "data_selector": "features"}},
            {"name": "collections", "endpoint": {"path": "collections", "data_selector": "collections"}}
        ],
    }
    yield from rest_api_resources(config)


def load_pystac_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pystac_pipeline",
        destination="duckdb",
        dataset_name="pystac_data",
    )
    load_info = pipeline.run(pystac_source())
    print(load_info)


if __name__ == "__main__":
    load_pystac_to_duckdb()

Run it with python pystac_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 PySTAC 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("pystac_pipeline").dataset()
df = data.search.df()
print(df.head())

SQL:

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SELECT * FROM pystac_data.search LIMIT 10;

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

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How do I deploy the PySTAC 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 PySTAC 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 PySTAC 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