Technologists have constructed distributed methods designed to course of a wide range of information sorts for a lot of use circumstances. Now we have key-value shops, relational databases, doc databases, graph databases, and even time-series database. However there’s one information and database kind that has largely eluded the fingers of expert builders: geospatial information. The oldsters at Wherobots who’re behind the Apache Sedona mission need to treatment that scenario.
As an affiliate professor of pc science at Arizona State College within the 2010s, Mohamed “Mo” Sarwat taught his college students in regards to the several types of databases and distributed methods. He lined the assorted attributes of the methods, akin to Apache Spark and Apache Flink, together with their strengths, weaknesses, and the tradeoffs that inherent on this line of labor. However there was one thing lacking when it got here to geospatial information.
“I checked out all of the methods that I taught, and all of the methods that I constructed and researched in my coaching within the area, and none of them handled geospatial as a first-class citizen,” Sarwat tells BigDATAwire. “They’re general-purpose methods, which is nice…However they didn’t present assist for geospatial information, for any features of the bodily world, even though a lot of the information exists out there may be collected from the bodily world.”
That’s to not say there have been no purposes designed for geo-spatial information. There are a variety of fashionable geographic info methods (GIS) purposes in the marketplace. Nevertheless, whereas these GIS apps are extensively adopted, they usually don’t present the form of distributed information administration and processing capabilities that as we speak’s massive geospatial information calls for, Sarwat says.
“I used to be these two worlds,” Sarwat continues. “You had the geospatial world on one hand and the information and the infrastructure work however, and so they had been talking totally different languages and had been going too many various instructions.”
Confronted with an absence available in the market, Sarwat and his ASU colleague, Jia Yu, who was within the PhD program, did what untold variety of technologists have accomplished earlier than them: They determined to construct it themselves.
A New Geospatial Framework
In 2017 at ASU, after intensive trial and error, the pair launched a framework known as GeoSpark that prolonged the Apache Spark framework with assist for geospatial information and processing.
The software program is designed to effectively ingest, remodel, and course of giant quantities of geospatial information, akin to that generated by satellites, GPS, telephones, cameras, and different sensors.
“It’s Apache Spark, however for bodily world information,” Sarwat says. “As we had been stepping into this sort of area, we tried plenty of issues, and it didn’t work out. That’s why the market did chew on it [Sedona], as a result of it didn’t exist in any respect. We lastly discovered one thing that may assist us try this, and that’s why there’s plenty of traction for the software program.”
Apache Sedona capabilities as a scalable information warehouse for geospatial information. Widespread GIS instruments capabilities like enterprise intelligence instruments that lets customers work together with geospatial information in a really detailed manner, however which lack the underlying distributed engine that permits customers to work with very giant geospatial information units.
Builders can make the most of Apache Sedona by way of commonplace utility programmer interfaces (APIs) for Python, which is the most well-liked technique to entry Sedona, or optionally by way of Spatial SQL, which is an extension of the SQL commonplace. The open supply mission additionally contains a software program improvement package (SDK) that Java and Scala builders can incorporate into their work. 
There are intricacies to dealing with geospatial information that different sorts of distributed engines don’t face. As an illustration, it’s very troublesome to type and index geospatial information, Sarwat says.
“A whole lot of this information is definitely polygonal geometries which are very, very intricate,” he says “Consider boundaries–not even static boundaries, like state or counties. I’m speaking about boundaries of buildings. I’m speaking about even shifting boundaries, off vehicles or off shifting objects. It’s not simply an X column and a Y column, for instance, in a desk. It’s rather more difficult than that.”
Processing these boundaries entails filtering objects and figuring out how they intersect with one another. These geometric computations are very compute intense, and it simply doesn’t work with conventional computing paradigms, Sarwat says.
“It’d work, however it is going to be very sluggish, very inefficient, and should not even scale to the scale of the information and the scale of compute to run on that information,” he says.
Enter Wherobots
The downloads of GeoSpark began at just some a whole lot at first, nevertheless it shortly cranked up into the 1000’s and shortly the hundreds of thousands. In 2020, Sarwat and Yu submitted Sedona to the Apache Software program Basis, and as of July 2025, Apache Sedona had been downloaded 15 million occasions. The uptake stunned them.
“To be utterly trustworthy, after we launched it as lecturers on the college, me and my college students, thought possibly like just a few different individuals internationally and different universities would begin utilizing it,” Sarwat says. “We realized there’s a hole, however we didn’t notice how massive of a niche that was. The market was very thirsty for a know-how like that.”
In response to the rampant enthusiasm for his or her mission, Sarwat and Yu did what an untold variety of technologists have additionally accomplished by means of historical past: They determined to create an organization round it. In 2022, they co-founded Wherobots to ship a hosted model of Apache Sedona, a la the connection Databricks initially had with Apache Spark.
As an alternative of attempting to run geospatial workloads as consumer outlined capabilities (UDFs) in an information warehousing setting, akin to Oracle, Databricks, or Snowflake, they will run the workload as a typical perform in a Apache Sedona cluster and get massive efficiency beneficial properties. In the event that they transfer their Sedona workload to Wherobots serverless cloud, which options greater than 300 pre-built raster and vector capabilities akin to map matching, geostatistics, and map tiles, they will see one other 30% to 50% in efficiency beneficial properties, Pruden says.
Huge Geospatial Use Instances
The wonderful thing about enhancing the processing of geospatial information is the variety of purposes that may be constructed. From insurance coverage and actual property to logistics and social media, there are all kinds of the way geospatial information may be included into an utility. Because the variety of information factors goes up, so too does the load on the underlying information infrastructure, which is the place Apache Sedona and its Apache Spark-based information processing capabilities are available in.
For instance, the last-mile supply downside is a big problem for firms like Amazon that try to ship packages to billions of individuals world wide. The amount of deliveries occasions the scale of the supply squad occasions the scale of the developed world equals a significant computational downside for Amazon. However because of Apache Sedona, Amazon is ready to deal with the problem.
Amazon offered on their use of Apache Sedona through the AWS re:Invent convention final 12 months, says Wherebots Director of Advertising Ben Pruden.
“They’re taking on this information from satellite tv for pc imagery, from aerial imagery, like from drones, from GPS traces coming off of their vehicles which are taking packages to your home, streetside imagery,” Pruden says, “and so they deliver it right into a system that’s largely powered by Sedona to do a really giant graph after which conflation of their information units to keep up these actually up to date maps of the world.”
Apache Sedona is essential for offering detailed map representations that Amazon drivers use to get instantly the correct place inside clients’ homes or residence complicated, Pruden says. “Or when you’re out in a rural space, possibly there’s a very lengthy driveway that isn’t apparent that it’s a must to determine the place to drive down,” he says. “They’re getting ready all that information throughout all the planet after which serving that again in order that their drivers can put it to use.”
One other early adopter of Apache Sedona is Overture Maps Basis, which is constructing an open reference map of all the world. The group began out working Apache Sedona on Spark, and up to now six months has been migrating to the Wherobots platform, Pruden says.
“Organizations like Overture and various others are utilizing each our open supply and likewise more and more Wherobots to research and ship perception and create information merchandise for information in regards to the bodily world,” he says.
Whereobots, which is predicated in Scottsdale, Arizona, remains to be ramping up cloud operations on AWS, which the corporate says is an in depth accomplice. The corporate raised $21.4 million in enterprise funding in November. Within the meantime, the corporate is trying ahead to the following frontier for geospatial information: integration with AI.
“To this point, AI has been actually good with language, responding to us. We work together with it, and it’s improbable,” Sarwat says. “However up till as we speak, AI doesn’t have an excellent information of the bodily world, like the way to motive in regards to the bodily world normally…And that is what we’re specializing in, on how can we offer an information engine that may make that sort of information AI prepared and make it very comprehensible by AI.”
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