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Welcome to This Month In Wherobots where we highlight the latest goings on from the Wherobots & Apache Sedona community. In this edition we’re taking a look at the largest release ever in the history of Apache Sedona, the latest SedonaDB and Wherobots Cloud launch, self-service geospatial analytics, building maps with Felt, and a look at the Wherobots Online Community.
Each month we highlight special members of the community who contribute their expertise and passion to the Wherobots and Apache Sedona community. This month’s featured community members are Nara Khou Lead Data Engineer at Land O’Lakes and Cort Lunke Data & Analytics Lead at Land O’Lakes. Earlier this year Nara and Cort presented “Self-Service Geospatial Analytics Built On Databricks, Apache Sedona and R” at Databricks’ Data+AI Summit. Thanks so much Nara and Cort for sharing your success story using Apache Sedona with the community!
In this presentation Nara and Cort discuss some of the challenges of working with spatial data and how Apache Sedona can be used to address some of these difficulties in an enterprise data environment like Databricks. Cort and Nara share why they chose Apache Sedona for working with spatial data at scale to analyze watershed and cropland data. They discuss the data processing pipeline used for the project and demo the end to end data pipeline from data collection, processing and analysis using Apache Sedona, and visualization using R-Studio – all within Databricks.
Watch the recording of Nara & Cort’s Data+AI Summit Presentation
The Wherobots team was excited to reveal SedonaDB, the cloud-native spatial analytics database platform at the FOSS4G North America conference. SedonaDB builds upon the scability and stability of the Apache Sedona project bringing large-scale geospatial analytics capabilities to enterprises looking for a cloud-native solution. SedonaDB also introduces the Havasu open table format which enables efficient querying and updating of geometry and raster columns in Parquet files stored in cloud object stores such as AWS S3.
Get started with SedonaDB on Wherobots Cloud Free tier today.
The most recent release of Apache Sedona v1.5.0 was the biggest release in the history of Apache Sedona. This release includes native support for Uber H3 hexagon functions, comprehensive raster ETL and analytics support, more ST functions to enable new geospatial workloads, XYZM support, and visualization with SedonaKepler and SedonaPyDeck. This version is also available in the official Apache Sedona Docker Image. You can find more about this release in the Apache Sedona GitHub repository and read more about the v1.5.0 Apache Sedona release here.
Learn more about the latest Apache Sedona release
An important requirement for data infrastructure tools like SedonaDB and Wherobots Cloud are that they integrate well with the technology ecosystems around them. In the world of spatial databases this includes geospatial visualization tooling like the web-based mapping tool Felt. This blog post shows how to integrate the Felt API with Wherobots Cloud so we can leverage SedonaDB’s geospatial analysis capabilities using Spatial SQL then publish the results of our analysis to Felt’s beautiful web-based mapping tooling. This example uses data from BirdBuddy, which publishes data about bird sightings at its smart birdfeeders to find the range of some of our favorite bird species.
Read the blog post “Creating Collaborative Web Maps With The Felt API And Wherobots Cloud”.
The Wherobots Online Community is the forum for community members to come together, ask questions, and share their expertise and excitement about spatial analytics. This site was launched earlier this month and we’re excited to have a home for the community. Please feel free to join the community, introduce yourself, and share what you’re working on and why you’re excited about spatial analytics! We’ve also launched the Wherobots YouTube Channel as a way to share educational content about spatial analytics – please check it out and subscribe.
Join The Wherobots Online Community
Get the latest news on Wherobots, Apache Sedona and geospatial data. Subscribe to the Spatial Intelligence Newsletter:
Key takeaways
Sedona 1.5.0, the SedonaDB and Wherobots Cloud launch, a Felt API mapping tutorial, Land O’Lakes’ Data+AI Summit talk, the new community forum and YouTube channel, and two upcoming events.
Native Uber H3 hexagon functions, comprehensive raster ETL and analytics, additional ST functions, XYZM support, visualization with SedonaKepler and SedonaPyDeck, and availability in the official Apache Sedona Docker image.
Nara Khou and Cort Lunke of Land O’Lakes, who showed a Databricks + Apache Sedona + R pipeline for watershed and cropland data at Data+AI Summit.
No. It is a monthly community newsletter that links out to the SedonaDB launch, Felt tutorial, Sedona 1.5 notes, and event pages published separately.
RasterFlow is now available in Public Preview
RasterFlow makes planetary-scale earth intelligence workflows easy and costs predictable. We are excited to announce that RasterFlow is now in Public Preview, opening up the power of planetary scale Earth Intelligence to all Wherobots Professional Edition customers! RasterFlow let’s you solve complex monitoring challenges with vision-language models or tailored models for specific use cases, without […]
Wherobots for QGIS: Cloud-Scale Spatial Data, from Wherobots Labs
Wherobots for QGIS is a new plugin that connects QGIS to Wherobots Cloud. From a panel inside QGIS, an analyst can run spatial SQL against WherobotsDB, load the results as a map layer, push a local layer up to a Wherobots Iceberg table, and pull raster data into the canvas. Why Wherobots for QGIS: Eliminating […]
How Wherobots builds with NVIDIA to let AI see the physical world
The world and what happens in it is digitized by petabytes of raw and derivative spatial datasets of various data types and scales, and the potential for applying AI to it is immense. But the AI models and agents we use every day need connectivity to tools that turn this data into usable insights and relationships. Wherobots gives AI the ability operate on and understand raw physical-world data, and NVIDIA GPUs are core to it. Architecturally here’s how this works at a high level.
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.
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