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Welcome to the April edition of the Spatial Intelligence Newsletter! This month, we’re covering the benefits of using Apache Iceberg, spatial joins, cloud-native geospatial, and new product updates like isochrones to help you make better location-based decisions. What does it all mean, and how can it help you increase data productivity? Check it out here! 👇
⏰💰Hurry, time is running out!
We’re currently offering a FREE $400 credit when you subscribe to the Professional edition of Wherobots, which includes exclusive features like GeoAI with WherobotsAI Raster Inference, map matching for cleaning messy GPS data, new travel isochrones for better location-based decisions, and the ability to bring your own cloud storage, just to name a few.
In addition, with the release of our drive-time isochrones, we’re now offering free access to Overture Places data—enriched with drive-time isochrones across every location in the U.S.—through our Pro tier data catalog. And we will be maintaining that dataset with every release in the future, so you’ll be able to use it going forward for location intelligence. There’s no obligation to get started, so be sure to take advantage of this (it’s like free money). Offer ends on May 31st, so don’t wait!
Benefits of Apache Iceberg for geospatial data analysis
🧊 Apache Iceberg support for GEO data brings a significant modernization for geospatial data and solutions. This support makes it easier for you to bring geospatial data into an open data architecture that decouples compute and storage and lower your costs. By adopting Iceberg in a data lake, you’re enabling your team to leverage the right tool for the job without needing to worry about locking your data into a vendor or a database solution that doesn’t scale.
Additionally, traditional file formats and row-oriented databases struggle when scaling beyond a million features, often performing poorly or only accommodating data that fits comfortably in memory. 😩 Iceberg, built on Parquet, solves this with lightning fast reads, scalability for larger-than-memory datasets, and developer friendly features like DML operations. Plus with added capabilities like versioning and time travel, users can query both current and historical data seamlessly. 🔍 Follow along this post to learn how to use Apache Iceberg with Sedona and find out how these features benefit spatial computations.
Cloud-Native Geospatial: More Than Just Big Data
e💡We had a very insightful discussion with Amy Rose (CTO) from Overture Maps and Eshwaran Venkat (CTO & Co-Founder) from Dotlas on cloud-native geospatial technology. Here are some highlights:
Spatial Joins at Scale: Unlocking Advanced Geospatial Analytics with Wherobots
🌎🤝 Spatial joins are essential for geospatial data analysis, but it can be slow or computationally expensive when working with large-scale datasets. Follow along in this tutorial as we walk through how easy and cost-effective it is to:
Sedona Success Story: Optimizing ETL pipelines at scale with Comcast
📊 Some of the challenges that Comcast was trying to overcome was data volume and repeatability. That’s why David Buchanan, GIS Architect, turned to Apache Sedona, which allowed him to reduce processing times from 5 hours to 30 minutes compared to GeoPandas. Watch the recording to learn more.
Apache Sedona Office Hours
😎 We just released Sedona 1.7.1, with some new features :
If you missed the office hour, check out the recording to learn more about the latest release. And don’t forget to mark your calendar for the next office hour! 🗓️
🆓 Both the Overture Places with isochrones dataset and isochrone functions, as well as the audit logs and job run monitoring, are available exclusively in the Pro tier. Take advantage of the free trial (ending soon!) to try these features and see how they can help solve some of the bottlenecks you might be facing when working with spatial data.
Geospatial Tables in the Open Lakehouse: A New Era for Iceberg and Parquet
Wednesday, May 5 at 9AM PT | Virtual
It’s easier than ever to work with geospatial data, with Iceberg and Parquet now offering powerful solutions for both geospatial experts and non-spatial professionals. Join this livestream with leaders from Foursquare, Databricks, Planet, and Wherobots as they discuss the historical challenges of handling spatial data, bridging the gap, and future adoption of these advancements.
Apache Sedona + Iceberg GEO Meetup
Monday, May 12 at 5:00PM PT | San Francisco, California
Join us for a fun and informative evening as we explore Apache Iceberg’s new native geospatial support, designed to solve major challenges in managing geospatial data at scale. This will be a great opportunity to connect with professionals in the field to learn about the latest developments in spatial data, as well as exciting projects people are working on.🌟 Featured speakers:
CNG Conference
April 30 – May 2 | Snowbird, Utah
We’re excited to attend the upcoming CNG Conference! Be sure to check out these sessions:
Day 1
Day 2
👥 If you’ll be at the conference, we’d love to meet with and chat about how you’re working with geospatial data. Feel free to reach out if you’d like to schedule a time to connect!
Key takeaways
Overture Places with 5/10/15/20-minute U.S. driving isochrones, ST Isochrone functions, audit logs, job-run monitoring (Pro+), a STAC reader (OSS and Community+), and customizable notebook idle timeouts (Community+).
A free $400 credit when subscribing to Professional Edition, described as ending May 31st, covering Pro features such as Raster Inference, map matching, isochrones, and customer cloud storage.
SQL GeoStats (ST_DBSCAN, ST_GLocal, ST_LocalOutlierFactor), broadcast joins for distributed KNN, a STAC catalog reader, an OSM PBF reader, and new functions such as ST_RemoveRepeatedPoints.
The newsletter cites GIS architect David Buchanan reducing processing from 5 hours to 30 minutes compared with GeoPandas.
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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