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Visualize Large, Changing Geospatial Datasets with
ArcGIS + Kinetica

Kinetica is able to power real-time visualization and analytics of geospatial data in ArcGIS - on a massive scale, and without the wait.
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Kinetica: The Database for Geospatial Analytics

Kinetica was built for fast analytics on large and streaming geospatial data. ArcGIS with Kinetica enables you to perform sophisticated analysis and visualization of spatio-temporal data without the wait.

Real-Time Intelligence

Kinetica's lockless architecture, distributed ingestion, and vectorized query enable you to work with numerous sources of spatial and streaming data. Kinetica allows for simultaneous ingest and query, and avoids the need for constant re-indexing and re-aggregations as new data changes the picture.

Advanced Geospatial Capabilities

Kinetica is capable of analyzing billions of objects as they move in real-time. Advanced geospatial functions, geo-joins, graph solving and matching makes analytics on spatial and time series data at scale easier and faster.

Lower TCO, Faster to Deploy

Kinetica's vectorized capabilities enable analysts and engineers to analyze and deploy systems quicker than ever. Vectorized algorithms allow for simpler data structures, which means less time engineering the data, more flexibility for exploring the data, and lower compute costs.

Radiant Advisors - Spatial and Time-Series Databases
"Kinetica outperformed PostGIS in every query and was the only database to pass all feasibility tests across geospatial, time-series, graph, and streaming."

Fast Visualization of Big Geospatial Data

Data can be visualized at virtually unlimited scale with Kinetica's Web Map Service (WMS)

One of the core challenges with displaying large amounts of geospatial information, is moving data from the database layer to the GIS layer. Serializing and moving millions to billions of objects from one technology to another takes time.

But geospatial data stored in Kinetica can be rendered in-database, into a small WMS image layers, for display on top of basemaps with ArcGIS. This approach is blazing fast, even on massive datasets. With every pan and zoom on the map, Kinetica dynamically re-renders images using your live data source and sends them to the map.

Multiple layers

Visualize and analyze different types of spatial data simultaneously. This feature is particularly useful for applications that require the integration of different types of spatial data.

Calculated Fields

Visualize and analyze different types of spatial data simultaneously. This feature is particularly useful for applications that require the integration of different types of spatial data.


With cross-filtering you can filter data on a map and then that data will simultaneously update in charts and tables elsewhere on the dashboard. You can cross-filter data from Kinetica directly in ArcGIS.

Class Break...

Visualize data using color-coded maps based on predefined classes. A class-break rendering enables you to take data from one or more tables and apply styling on a per-class basis.

ArcGIS + Kinetica

Innovation through Integration

Kinetica now integrates easier with ArcGIS to bring together these complementary technologies to modernize IoT workloads. Data in Kinetica can be made available to ArcGIS through ArcGIS JDBC connector and through the ArcGIS Python API. Users can then use ArcGIS as a visual analytics environment with all the relevant functionality necessary to process, analyze, and display spatial data precisely and accurately.

A demonstration of how to leverage Kinetica and ArcGIS to access multi-billion row datasets, perform analytics, and display the results on the map canvas.
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White Paper

Build Real-time Location Applications on Massive Datasets

Vectorization opens the door for fast analysis of large geospatial datasets
Download the white paper

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.