Unlock New Insights with
Kinetica and ArcGIS
Esri’s ArcGIS is widely recognized as a powerful solution for creating, managing, and analyzing geospatial data. However, as the world embraces the Internet of Things (IoT) and data becomes more accessible across time and space, ArcGIS is limited. That's where Kinetica comes in. Meet Kinetica, an innovative database designed to handle large streaming databases with built-in powerful and fast spatial capabilities.
On-Demand Webinar
Unleashing the Power of ArcGIS and Kinetica Bridging the Gap Between GIS and Big Data ChallengesWatch Now »
Innovation Through Integration
Imagine endless possibilities that emerge when Kinetica and ArcGIS seamlessly integrate, eliminating the need for duplicating or aggregating data. Your expansive geospatial datasets no longer hinder your queries and analysis, but instead, become an integral part of your GIS, allowing you to unravel patterns, relationships, and anomalies quickly for immediate action.
The Possibilities Are Limitless
Organizations such as FAA, Ford, T-Mobile, and others are harnessing the power of Kinetica to tackle the most formidable spatial and time-series challenges. With Kinetica's innovative design, handling massive and streaming datasets becomes a breeze, even with billions of rows.
Key Strengths of Kinetica
Geo-Joins
Extensive support for geo-joins like contain, within-a-distance, intersect, overlap, and more
Advanced Geospatial Analytics
Advanced geospatial analytic functions like st_, stxy_, entity tracks, and more.
Graph Capabilities
Graph matching and solving capabilities
Kafka integration
Native Kafka integration and distributed ingest for real-time processing
Web Mapping Service (WMS)
For server-side rendering of heatmaps, contours, tracks, unique value, and classbreaks on billions of records
Book a Demo!
The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.
Contact us, and we'll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.