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We’re excited to announce that Dekart now supports WherobotsDB as a spatial SQL engine, enabling you to execute high performance spatial queries while rapidly visualizing query results on a map all within the Dekart platform.
If you’d like to skip the blog post and jump right in, you can follow our Getting Started Guide here.
Dekart is a geospatial analytics application, available as a managed platform or self-hosted, that turns SQL queries into shareable, interactive maps. It glues a slim Golang backend onto the Kepler.gl library, letting analysts connect directly to their cloud data warehouse and visualize millions of rows without exporting files or writing code.
Dekart gives data teams a fast path from SELECT… to a production-ready map, without the overhead of proprietary GIS stacks like ESRI or heavyweight desktop software. If your spatial analysis already lives in SQL, Dekart is a practical, lightweight way to visualize and share it, all from your browser.
This integration allows customers to use Wherobots as the query engine to create visualizations in Dekart. Here’s why the combination matters.
This architecture is especially powerful for teams who work with large, dynamic spatial datasets such as:
These datasets can often change daily or hourly. With the integration between Wherobots and Dekart, you don’t need to rebuild maps from scratch. Just re-run your Spatial SQL query and get an updated visualization from the versioned data in your cloud lakehouse.
This is what turns Kepler.gl from a static exploration tool into a repeatable, enterprise-grade intelligence system for the physical world.
And because both Wherobots and Dekart are built on open standards and open source foundations (Sedona, Iceberg, Kepler), you’re not locked into a proprietary GIS ecosystem.
Spatial data is the underpinning of the telecommunications industry. From cell tower coverage to network congestion to user demand mapping, telcos generate a continuous stream of location-rich telemetry. This data is used for maintaining quality of service, rolling out infrastructure, and responding to outages. But processing this much data at scale and making sense of it often presents a challenge.
Many telcos already use Apache Sedona in data lakehouses like Databricks to process call records, analyze tower placement, and perform network optimization. These workloads can involve billions of records and highly complex spatial joins. Look no further than how Comcast is using Apache Sedona here.
With Wherobots and Dekart, telco data teams can now take this one step further:
Here’s an example:A network operations team wants to analyze dropped call patterns across the San Francisco Bay Area. They write a Spatial SQL query in Wherobots that joins call records to a hex grid, then apply Getis-Ord Gi* to identify statistically significant clusters of poor performance. Within seconds, Dekart renders the output as an interactive map—highlighting not just where drop-offs occur, but where they represent systemic problems.
No exporting data. No manual mapping. Just fast, repeatable insights from data about the physical world.
Historically, you would use the same platform to both process and visualize spatial data. This often resulted in mediocre performance on both of those tasks.
At Wherobots, we believe that the optimal workflow is having a dedicated spatial data processing platform (like Wherobots) that you can pair with the visualization tool that works best for your use case.
That’s why we are particularly excited by this integration with Dekart. It gives our users yet another in our growing list of visualization tools they can layer on top of Wherobots to support their day-to-day spatial workflows.
We believe that the modern spatial stack should use the best-in-class tool for each step in a spatial workflow. In this case, Wherobots for spatial data processing and querying, Dekart and other solutions for visualizing on a live map.
That’s why we support:
We also allow users to generate VTiles (a type of map tile from vector data) from any spatial dataset using SQL. Those tiles can be exported as PMTiles–a performant, portable, cloud-native tile format for serving map tiles. These PMTiles can be used with MapLibre, Leaflet, or any WebGL tile renderer.
Whether you’re optimizing communication networks, mapping field boundaries, analyzing human movement, or publishing public map layers, Wherobots and Dekart provide a fast path for converting spatial data into insights. Create your Wherobots account here.
To get started with Wherobots and Dekart, you can follow our Getting Started Guide here.
Or for a quick view of what’s involved, take a look at this video where Dekart founder Volodymyr Bilonenko walks through connecting Dekart to Wherobots and running a query.
Let’s build the next generation of intelligence for the physical world.
Key takeaways
A geospatial analytics application, offered as a managed platform or self-hosted, that turns SQL into shareable Kepler.gl maps. A slim Golang backend lets analysts connect to a cloud warehouse or Wherobots and visualize millions of rows in the browser.
Dekart uses WherobotsDB as the spatial SQL engine. You write Spatial SQL in Dekart, Wherobots processes data in your S3 lakehouse, and Dekart maps the result. The post cites up to 20x faster joins/filters than other cloud lakehouse engines.
Yes. The post walks through telco telemetry—cell coverage, congestion, dropped calls. An example analyzes Bay Area dropped-call patterns with a hex grid and Getis-Ord Gi* hotspots, then renders the map in Dekart without a GIS desktop.
Follow the Getting Started Guide linked in the post, or watch the video of Dekart founder Volodymyr Bilonenko connecting Dekart and running a query. You will need a Wherobots account.
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