Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
Pranav is building the next wave of scalable geospatial analytics engine at Wherobots. He is passionate about enabling spatial intelligence to solve planetary-scale problems and democratizing the power of geospatial data through open-source software like Apache Sedona.
Iceberg v3 Gets Native Geo Types. It’s More Than a Format Upgrade
Introduction Geospatial data touches nearly every industry, and until recently, the open lakehouse had no native way to handle it. Snowflake recently announced Iceberg v3 support with native geometry and geography types. It’s the first major engine to ship the geospatial extensions to the Iceberg spec. These types are now part of the open standard, […]
Raster Processing at Scale: The Out-of-Database Architecture Behind WherobotsDB
Learn how WherobotsDB's out-of-database architecture processes terabyte-scale satellite imagery, elevation models, and sensor data at scale, enabling zonal statistics, raster algebra, and planetary-scale AI inference without custom infrastructure.
Scaling Spatial Analysis: How KNN Solves the Spatial Density Problem for Large-Scale Proximity Analysis
How we processed 44 million geometries across 5 US states by solving the spatial density problem that breaks traditional spatial proximity analysis
Introducing wkls: A Python Library for Instantly Accessing Global Administrative Boundaries
Why Defining Administrative Boundaries in Spatial Data is Hard If you’ve ever worked with spatial data, you probably needed to define a geographic boundary within which to conduct your analysis. Most of the time, these are administrative boundaries such as cities, states, provinces, countries etc. For instance, if you want to scope your analysis to […]
Location Intelligence with Isochrones: A Complete Guide
Ditch intuition: Use data to optimize location decisions. Where you invest has a long-term impact on a location’s overall profitability. The mantra of “location, location, location” is absolutely still alive. But unlike 20 years ago, you now have the ability to let off-the-shelf data and modern geospatial engines like Wherobots tell you where the ideal physical locations are to invest in, across hundreds of thousands of options, using a few lines of SQL.
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.
Overture Data In Wherobots Spatial Data Catalog
Effortlessly access and analyze Overture Maps Foundation data with Wherobots. This guide details seamless integration, leveraging our Havasu Iceberg spatial catalog for optimized performance and insightful visualizations. Explore practical examples and unlock new possibilities in geospatial data analysis.
Wherobots and PostgreSQL + PostGIS: A Synergy in Spatial Analysis
TL;DR Wherobots (powered by Apache Sedona) and PostgreSQL + PostGIS are complementary tools, not competitors. Use Wherobots for large-scale spatial ETL, processing speed, and distributed analytics. Use PostGIS for data persistence, transactional integrity, and long-term storage. Together, they form a complete spatial data pipeline. At scale, WherobotsDB is up to 317x faster than PostGIS on […]
Analyzing The Overture Maps Places Dataset Using Apache Sedona, Wherobots Cloud, & GeoParquet
Explore how to analyze the Overture Maps Places dataset using Apache Sedona, Wherobots Cloud, and GeoParquet. Learn how GeoParquet enhances geospatial analytics and unlocks insights into urban dynamics with real-world use cases.
