Compute Planetary scale spatial data operations WherobotsDB, fully compatible with Apache Sedona, is the engine built for physical world data. TRY WHEROBOTS Optimized for Geospatial High performance spatial joins with 300+ ST_ and RS_ functions built to accelerate productivity. Unparalleled Performance Up to 20x faster geospatial operations than traditional big data engines. Planetary Scale Powered by Apache Spark and Sedona to scale your spatial intelligence without limits. The most complete spatial data engine WherobotsDB delivers the fastest and most capable engine for cost-efficient performance across general-purpose spatial SQL workloads. The following table was run across multiple compute engines using the Apache Sedona SpatialBench at Scale Factor 1000. Scale Factor 1000 Query Capability Matrix SpatialBench Query # WherobotsDB Sedona on Serverless Spark Proprietary SQL Serverless Proprietary Spark Clusters with Spatial SQL Sedona on Managed Spark Clusters 1: Spatial filter, aggregation, sorting 2: Spatial filter, aggregation, sorting 3: Spatial filter, aggregation, sorting 4: Lightweight spatial join and aggregation 5: Spatial aggregation 6: Heavy spatial join 7: Geometry construction and access 8: Heavy distance join 9: Heavy polygon self spatial join 10: Heavy spatial left join 11: Multi-way spatial join 12: KNN join SpatialBench for Apache Sedona Key capabilities Unlimited Scale Scale workloads seamlessly from small to a planetary scale. Apache Spark & Sedona Compatible Migrate existing Apache Spark and Sedona workloads without changing a single line of code. Fully compatible out-of-the-box. Nothing to Manage But Your Code Serverless by default, or deploy securely in your own VPC. Complete Spatial Intelligence Cloud High performance spatial joins with 300+ ST_ and RS_ functions built to accelerate productivity. Low Cost + High Performance Spatial Operations WherobotsDB delivers fast, cost-efficient performance across spatial SQL and python workloads*. *This represents actual cost benchmarking results for SpatialBench queries 7-12 with a scale factor of 1000 across three of the most comparable deployment options. Contact us to Compare All Benchmarks WherobotsDB Lowest Proprietary SQL Serverless Engine x2.92 higher Apache Sedona on Serverless Spark Engine x2.03 higher Proprietary Cloud Managed Spark x2.52 higher Relative cost across operations that completed Workload Flexibility From Small to Planetary Scale Workloads Perform city to global scale spatial data operations in a single solution. City Region Country Global Productivity Accelerate Innovation Economically SQL and Python 300+ ST_ and RS_ Functions Raster Vector Tabular SPATIAL JOIN Data Products “Spark is incredibly powerful — but it’s also a huge learning curve, Wherobots shortened the painful part of Spark and gave us production-grade scalability without having to babysit clusters.” Ben Hudson Co-founder and Head of Applied Science, aarden.ai Apache Spark & Sedona Compatible Lift and shift your existing Spark + Sedona workloads with the same APIs and code, now with improved performance and lower cost. Learn More scalaspark.read.format("csv") .option("header", "true") .load("cities.csv") .createOrReplaceTempView("cities"); val result = spark.sql( "SELECT * FROM cities WHERE ST_Contains(area, point)" ) result.show() Deployment & Compatibility Zero Migration. Total Flexibility. Keep your existing Apache Spark or Sedona code, run it serverless or in your VPC Serverless by Default Zero infrastructure to manage Auto-scaling, cost-efficient Works out-of-the-box Learn more Deploy in Your Cloud (BYOC) Full control in your VPC Enterprise-ready security Works behind your firewall Reach out to our team Get Started with WherobotsDB Explore the platform, build powerful solutions, or automate your geospatial workflows. Discover Seamless access to the right datasets, AI models and solution accelerators for your use case. LEARN MORE Build Start using WherobotsDB with familiar Apache Spark/Sedona syntax. LEARN MORE Automate Orchestrate spatial data flows at scale with Apache AirFlow or our REST API. LEARN MORE FAQ What deployment options are available for Wherobots? Wherobots provides two primary deployment options: a SaaS Serverless model and a Bring-Your-Own-Cloud (BYOC) deployment option. In the serverless model, which is the default, Wherobots manages both the control plane and the multi-cloud/multi-region compute plane in its own environment. For customers with specific security or regulatory needs, the BYOC option allows the compute plane to be run within their own cloud Virtual Private Cloud (VPC). Which spatial functions are supported in Wherobots? Wherobots has built-in support for over 300 spatial functions, including standard ST_ and RS_ functions for geometry processing, spatial predicates, and large-scale joins. In addition, Wherobots supports a wide range of advanced spatial functions for scalable analytics, including: Spatial joins (point-in-polygon, polygon-to-polygon) kNN join for nearest neighbor queries Zonal statistics on raster layers Travel isochrones to define travel areas within a specified time GPS map matching for real-time mobility data Raster inference with deployed AI models PMtiles generation for fast map visualization What raster RS_ function types are available in WherobotsDB compute? 100 and growing Raster RS_ functions are available in WherobotsDB including the following types: In-DB and Out-DB raster loaders Affine Transformations including translation, scaling, rotation, shearing, reflection as well as additional mathematical functions for collinearity, concurrency and ratios of segments. Union Aggregation to blend / combine multiple rasters into a single multi-band raster. Raster functions such as pixel operations, geometry functions, accessors and band accessors, zonal stats, predicates, operators, map algebra, and tilers. Writers including dataframe, ArcGrid, Geotiff, and PNG Visualizers for interacting with output results of operations How does Wherobots out-perform traditional big data engines for spatial queries? Wherobots is built for spatial data workloads, utilizing a distributed architecture based on Apache Spark and Sedona that has been optimized at the compute, query planner, and within each function type levels. This results in significant performance gains, with teams reporting up to 20x better performance for spatial joins and other complex operations compared to general-purpose big data engines. It also separates compute from storage for optimal efficiency. Get started Modernize your spatial data in the lakehouse today. request demo try wherobots now
