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Meet Fused

Fused is the glue layer to run workflows to load data across your most important tools.

Use the Fused serverless API to build, scale, and ship geospatial workflows of any size.

Why Fused?​

Geospatial datasets are growing in size and complexity. Traditional methods to access and process data are becoming increasingly impractical for the needs of academia and industry. Cloud-optimized data formats and serverless present a much-needed solution.

Ecosystem​

Build any scale workflows with the Fused Python SDK and Workbench webapp, and integrate them into your stack with the Fused Hosted API.

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Patterns​

User Defined Functions (UDFs) are building blocks of serverless geospatial operations that integrate across the stack. They connect frameworks such as Planetary Computer, Google Earth Engine, Big Query, Snowflake, and DuckDB, as well as cloud-native datasets such as NASA, NOAA, US Census, and Overture. With Fused, users write, share, or discover UDFs that turn into live HTTP endpoints that load their output into any tools that can call an API.

The @fused.udf decorator is used to define a Python function as a UDF. This simplified example illustrates the concept:

@fused.udf
def my_function():
    ...
    return df

Keep these fundamentals in mind as you work with UDFs.

  • @fused.udf: The UDF decorator prepares the UDF to be deployed as a serverless function that can be invoked through HTTP requests.
  • @fused.cache: UDFs can import from any Python library or custom helper modules, and cache the output of helper functions with the cache decorator.
  • File and Tile execution modes: There's two types of UDF: Tile loads data as a collection of tiles at various zoom levels that make up a complete map, and File loads a single output object.
  • Return types: UDFs can return any serializable data object. Note that for the output to render on a map, it should be a raster or vector type.

Connect with the community​

Write, share, or discover UDFs across the Fused ecosystem.