Pystac Client Python API Docs | dltHub

Pystac Client is a Python client for interacting with STAC Catalogs and STAC APIs, providing helpers for opening STAC roots, searching, paging, and retrieving Collections and Items. The REST API base URL is https://earth-search.aws.element84.com/v1 and All requests require a Bearer token provided in the 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 Pystac Client data in under 10 minutes.

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What data can I load from Pystac Client?

Here are some of the endpoints you can load from Pystac Client:

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How do I authenticate with the Pystac Client API?

Authentication is provided via custom HTTP headers (e.g., Authorization: Bearer ) passed to StacApiIO or the Client's request_modifier.

1. Get your credentials

1. Visit the provider’s website (e.g., https://planetarycomputer.microsoft.com or https://earth-search.aws.element84.com).
2. Sign in or create an account.
3. Navigate to the API/Token section of the dashboard.
4. Generate a new API token or locate the existing token.
5. Copy the token and store it securely (e.g., in an environment variable or secrets.toml).
6. Supply the token as a Bearer header in your PySTAC‑Client code.

2. Add them to .dlt/secrets.toml

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[sources.pystac_client_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 Pystac Client 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 pystac_client_pipeline.py

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

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Pipeline pystac_client_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pystac_client_data
The duckdb destination used duckdb:/pystac_client.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 pystac_client_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 search and collections from the Pystac Client 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 pystac_client_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://earth-search.aws.element84.com/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "search", "data_selector": "features"}},
            {"name": "collections", "endpoint": {"path": "collections", "data_selector": "collections"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pystac_client_pipeline",
        destination="duckdb",
        dataset_name="pystac_client_data",
    )
    load_info = pipeline.run(pystac_client_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("pystac_client_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pystac_client_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pystac_client_pipeline").dataset()
data.search.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 Pystac Client 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 failures

Ensure you pass an Authorization header (e.g., Bearer token) via Client.open(..., stac_io=StacApiIO(headers={"Authorization":"Bearer <token>"})) or using request_modifier. Verify that the token is still valid with the provider.

Pagination and method fallback

ItemSearch uses POST by default; if POST returns a 405, the client automatically retries with GET for subsequent pages. Control page size with limit or max_items; results are returned in the features array and pagination follows the links provided by the STAC API.

Missing /collections or /search support

Not all STAC roots implement these endpoints. When the endpoint/link is absent, the client raises NotImplementedError. Check the root catalog's links to confirm available endpoints before invoking them.

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