Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
Authors
The EU is widely recognized as a world leader for climate solutions, automotive design and manufacturing, mobility systems and analysis, environmental monitoring, agriculture and precision farming, and urban development. Geospatial data is foundational to the success of these innovations. However most of the new technology is developed using tools and cloud services not optimized for geospatial development. Compared to internet data, support for geospatial data in the modern cloud environments has lagged. This technology gap has made working with geospatial data expensive, and required staffing data teams with unique expertise.
We are excited to announce that Wherobots is ready for EU native workloads. This expansion makes it possible for many EU companies to approve the use of Wherobots and adhere to their data residency requirements by processing and storing data in-region.
Wherobots’ mission is to make it easy for our customers to utilize geospatial data. We are delivering on it via a cloud optimized for developing and running solutions about the physical world, at any scale. Our purpose-built, cloud native approach is enabling teams at AddressCloud and Overture Maps Foundation to accelerate their pace of innovation with geospatial data. Their workloads run up to 20x faster after migrating from popular cloud-based engines, developer productivity is boosted with the most feature complete development experience for SQL and Python, and costs are reduced, putting new solutions in reach.
The architecture of Wherobots is cloud native, and is deeply rooted in open source. Apache Sedona, the open source geospatial engine for Apache Spark, Apache Flink, and Snowflake, is 100% compatible with Wherobots. Users can easily lift and shift their Apache Sedona based applications into Wherobots with zero code changes. Wherobots is also one of the leading companies bringing GEO support into popular open file and open table formats like Parquet and Iceberg, and uses these formats by default. That way, you can deploy various engines on your data, benefit from the advantages of a Lakehouse engine such as ACID transactions and table versioning, without locking your data into proprietary vendor siloes, or inelastic solutions that couple storage with compute.
Getting started is easy. To use Wherobots within the AWS Europe (Ireland) region, get srated with the Professional Edition on the AWS Marketplace. Create a notebook and explore one of many examples designed to help you realize what you can create using SQL and Python. There’s no infrastructure to manage. Teams just use and pay for Wherobots usage on-demand via Wherobots Spatial Units, which reflect the amount of serverless computation consumed.
If there are other clouds or regions that you’re interested in using beyond the ones we currently support, please reach out to us at product@wherobots.com or fill out this form here. You can read more about Wherobots on the website or by exploring our product documentation.
Key takeaways
Yes. This post announces availability in the AWS Europe (Ireland) region so EU companies can process and store data in-region.
That is the stated purpose: EU-native workloads can keep processing and storage in-region to satisfy data-residency requirements.
Yes. Wherobots is 100% compatible with Apache Sedona on Spark, Flink, and Snowflake, so applications lift and shift with zero code changes. Data stays in open Iceberg/Parquet rather than a proprietary silo.
Subscribe to the Professional Edition on the AWS Marketplace, create a notebook, and run the example notebooks. Usage is billed on-demand in Spatial Units. Ask product@wherobots.com if you need other clouds or regions.
RasterFlow Public Preview: Planetary-Scale Earth Intelligence with Predictable Pricing
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 needing to manage complex inference infrastructure. Teams are already running […]
How to score every building in a state for catastrophe risk: an exploration project
Part 1 of a series: What’s possible? I didn’t start this project to build a real risk model, not one that can be used by an insurer tomorrow. But it is a workflow that an insurer can put into practice to create their own risk scores with Wherobots and their own expertise. The idea was […]
Learning Wherobots by Building a National AI Data Center Suitability Report
This guest post is from a Wherobots user George Chandeep Corea, which covers an exploration of how he used Wherobots MCP with his preferred AI coding tools to build the interactive AI data center site suitability analysis tool below. The following quote is from his own biography. Learning by Doing Below is a working national […]
share this article
Awesome that you’d like to share our articles. Where would you like to share it to:
