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Time & Space Analytics
at Speed, at Scale

Kinetica is ideal for real-time analysis on large, streaming spatial datasets. Kinetica's suite of spatial and time-series functions make it easy to do time-series and spatial processing in-database. This helps you avoid data movement, reduce latency and minimize costs.

For Advanced Time-Series and Geospatial Analytics

Spatio-temporal data requires new capabilities to join, analyze, and visualize in order to extract value.

Low Latency Joins
Context matters. Sensor data must often be combined with historical, integrated data without sacrificing data freshness. Kinetica makes it quick and fast to join high-cardinality data on inexact keys.

Time-Series & Spatial Functions

Tried and tested functions for working with times and datws and over 130 OGC high-performance geospatial functions are available with SQL or using the REST API. Functions include tools to filter, compare or aggregate data by area, by track, or custom shape.
Spatial Funtions »

Server Side Visualizations

Exporting large volumes of data to the front-end for rendering is expensive and slow. Kinetica generates server-side visualizations that can be sent to the front end as WMS tiles.
Window Functions »

Low Latency Spatio-temporal Joins

The shape of time-series and spatial datasets create headaches for most analytics databases. Kinetica's vectorized architecture is optimized for working with these sorts of data feeds.

Temporal Joins

How do you combine data from multiple tables when the timestamps don't exactly match?

Kinetica's ASOF joins make it easy to set an interval within which to combine data on values that are close to each other. This makes it easier to understand what's happening even when there aren't exact matches on timestamps from the two tables. You can use inexact joins for location, to connect tables where objects are within a specified distance of another.

Spatial Joins

Geo joins enable you to fuse two or more datasets based on spatial matching conditions. Most sensors and IoT devices record location as a set of longitude and latitude pairs. Modern sensor analysis often requires this point based location stream to be enriched with datasets that might represent information using other types of spatial geometries like polygons and lines. Geo joins make this easy to do.

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."

In-Database Functions for Time-Series & Spatial Analysis

To run powerful spatial and time series analytics at scale and speed, it's better to do the processing in-database to avoid data movement to separate systems that only increase latency and add complexity.

Time Series Functions

Kinetica supports windowing which allows you to apply aggregate and ranking functions over a period of time, and keep the picture updated as data evolves.

Work with timestamps and dates with ease. Kinetica includes base date/time functions along with tools for more complex date-time conversions.

Window Functions and Date-time Functions

Geospatial Functions

Kinetica natively works with points and shapes as WKT. Tracks can be generated from sequential data to represent paths objects take as they move.

Over 130 OGC high-performance geospatial functions are available with SQL or using the REST API. Functions include tools to filter, compare or aggregate data by area, by track, or custom shape. Kinetica's network graph functionality enables you to model location data as graphs, and then solve routing and other questions with a selection of in-built graph solvers

See Spatial Funtions and Kinetica Graph

Server-side Visualizations

Generate sophisticated WMS map overlays of large datasets directly on the server

One of the core challenges with displaying spatial information is moving data from the database layer to the visualization layer. Serializing and moving millions to billions of objects from one technology to another takes time.

Kinetica is able to solve this bottleneck by generating geospatial tiles directly on the server through a Web Mapping Service (WMS). WMS tiles can be used as overlays on top of a map with applications such as ESRI and Mapbox, Leaflet and others.

ESRI & Kinetica »
Web Mapping Service »

PLAY Tech Talk: When your application is a Map

Animation showing Twitter 4bn posts color coded by year. Even through the web it takes seconds to filter data by arbitrary geometries and interact with individual points. <br><br><a href="/product/demo/">Try it out yourself</a>

Billions of Points...

Display unlimited points on a map with interactice query. While noisy, this provides a unique means to explore volumes of data that is not possible when generated by the front-end alone.


Heatmaps take the noise out of large datasets to show patterns and nodes of maximum usage. Hatmaps generated in-database are another efficient.


Isolines represent curves of equal cost, with cost often referring to the time or distance from a starting point. Isochrones work with Kinetica's Graph functionality to visualize distances.

Class Break...

A class break rendering enables you to take data from one or more tables and apply styling on a per-class basis.

Try Kinetica Now: Kinetica Cloud is free for projects up to 10GBGet Started »

Leverage graphs for optimal routes or for network planning

Kinetica's graph API enables you to model spatial data as graphs. Then solve difficult questions using SQL queries, or with in-built graph solvers. Outputs from solvers can be piped directly to maps.


Property Queries

Find hidden relationships in your data instantaneously. The adjacency query engine is capable of traversing millions of graph nodes in many-to-many fashion with performance at scale.


What is the shortest path from point A to point B, factoring in speed limits, traffic, and other restrictions? Or figure out the best route to visit multiple destinations. Kinetica comes with a suite of graph solving features to make this easy.

Map Matching

Match data to networks. You can use Kinetica's map matching tools to determine roadways and paths from noisy GPS data.

Connect to your choice of tools

Kinetica plays well with a wide variety of BI and GIS tools. Or you can work with data through Kinetica Workbench and Kinetica Reveal.

BI & Observability Tools

With Kinetica's implementation of the Postgres Wireline protocol, popular observability and BI tools including Grafana, Prometheus, Datadog, Lookr and others can monitor and integrate directly with Kinetica.

Posgtres Wireline »


Data in Kinetica can be made available to ArcGIS Insights through ArcGIS JDBC connector and through the ArcGIS Python API, and visualizations passed back through WMS tiles. This enables ArcGIS to be used with streaming location data at scale.

ESRI Integration »


Kinetica's native extension for Tableau allows for geospatial processing to be done in-database with visulizations delivered back to Tableau through the WMS endpoint.
Tableau Connector »

Custom Applications

Kinetica's REST API with language specific implementations for Python, Javascript, Java, C++, C# and others enable flexible integration with custom applications and including ESRIs ArcGIS tools.


Kinetica Reveal - Native BI Framework

Kinetica comes with Reveal — a web-based BI framework that makes it quick and easy to start exploring geospatial data. Reveal also connects with Kinetica's geospatial pipeline for advanced mapping and interactive location-based analytics.

Learn More »


Kinetica Workbench

Kinetica Workbench is a sophisticated, yet intuitive interface, to interactively explore data, organize and store SQL workbooks, import and export data streams, and for general database administration.

Learn More »

Recent Webinar: Advanced Analytics and Machine Learning with Geospatial Data Watch Now »
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