Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
Ben lead's the go-to-market team at Wherobots. He has worked in distributed compute and SaaS applications for over a decade and enjoys customer storytelling, creating mutual value, and building innovative solutions on the edge of what's possible.
Dekart Supports Wherobots as a Spatial SQL Engine
With Dekart now supporting Wherobots as a Spatial SQL engine, this combination creates a snappy, highly scalable query visualization experience for spatial data in your lakehouse.
Advancing the Integration of Map Data via Overture’s Global Entity Reference System and Wherobots
The general availability of Overture’s Global Entity Reference System (GERS) makes it a lot easier to build intelligence about features of our physical world.
Accelerating Vector and Raster Analysis at GeoPostcodes
How long is too long to wait for a data set to process? In the fast-paced world of data as a service, efficiency isn't just a nice-to-have; it's essential. But for GeoPostcodes, previous updates to the global population movement datasets took about 39 days. This was running on a headless QGIS instance in combination with PostGIS, combining population rasters with postal code boundaries.
What is Apache Sedona?
Apache Sedona is a cluster computing system for processing large-scale spatial data. It treats spatial data as a first class citizen by extending the functionality of distributed compute frameworks like Apache Spark, Apache Flink, and Snowflake. Apache Sedona was created at Arizona State University under the name Geospark, in the paper “Spatial Data Management in Apache Spark: The GeoSpark Perspective and Beyond.
