Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
Jia Yu is the co-founder and Chief Architect of Wherobots. He also serves as the PMC Chair of Apache Sedona.
El Niño 2026, atmospheric rivers, and California’s burn scars: one SQL engine, two data models
7,713 mapped building footprints sit inside or within 500 m (1,640 ft) of the 42 Eaton basins USGS rates high hazard for debris flows, and 388 km (241 mi) of mapped road and path cross the low ground below the scar. WherobotsDB finds both in SQL: a join to the USGS basins for the buildings, and a raster vector join over elevation for the roads.
Measuring the Strait of Hormuz shutdown with Sentinel-2, WherobotsDB and Overture Maps
WherobotsDB read 719 Sentinel-2 scenes in place, fetched about 1.5 GB of the 151 GB, and joined the detections against Overture Maps land polygons to measure a 95% fall in traffic through the Strait of Hormuz corridor. Loading at Kharg and waiting at Fujairah held at 2025 levels, and the analysis cost about $63.
From the Spokane firestorm to all of Washington: real-time wildfire monitoring for under $50 a pass
The moment a satellite pass lands, this pipeline turns it into a burn-severity map in 14 minutes: real-time wildfire monitoring with Python and Spatial SQL that protects lives and assets, shown on the Spokane firestorm and scaled to the entire state of Washington.
WherobotsDB is 3x faster with up to 45% better price performance
The next generation of WherobotsDB, the Apache Sedona and Spark 4 compatible engine, is now generally available.
Introducing SedonaDB and SpatialBench for Apache Sedona
The spatial-first query engine and benchmarking framework for Apache Sedona
Iceberg GEO: Technical Insights and Implementation Strategies
In our previous blog post, we announced Apache Iceberg and Parquet’s support for spatial data types and discussed their significance. Today, we take a closer look at these GEO data types in Iceberg (collectively Iceberg GEO in this blog), exploring design, key features, and implementation considerations.
Apache Iceberg and Parquet now support GEO
Geospatial solutions were thought of as “special”, because what modernized the data ecosystem of today, left geospatial data mostly behind. This changes today. Thanks to the efforts of the Apache Iceberg and Parquet communities, we are excited to share that both Iceberg and Parquet now support geometry and geography (collectively the GEO) data types.
Building a Spatial Data Lakehouse
Explore how Apache Iceberg based Havasu redefines data management for geospatial data lakehouse architectures. Learn to optimize the storage, querying, and analysis of large-scale spatial datasets with high performance and cost efficiency.
Announcing Our 21.5M Series A :: Unlocking Answers to Planetary-scale Questions.
Each day, satellites, drones, applications, and GPS devices generate petabytes of spatial data that can be used to solve real-world problems. But the majority of such data is often stuck in siloed legacy systems or sits idle and disjointed. That’s why we have dedicated our academic and professional careers to answer planetary-scale questions. We’re on a mission to help companies make sense of their data so they can take on issues like how to manage their fleets of vessels and vehicles, where and how to build infrastructure, and determine the best methods to assess and mitigate risk of catastrophic natural disasters.
