The Database
for Time & Space
Time & Space Analytics
Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale.
Real-Time Insights
Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands.
Lower TCO
Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data.
Time and Space AnalyticsFor Complex Geospatial & Temporal Questions
Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale.
Geospatial Functions
Work with with geospatial objects (points, shapes, tracks) and take advantage of over 130 vector-enhanced geospatial functions for filtering and joining data by area, by track, custom shapes and more.
Geospatial Analytics »
Visualization Pipeline
Native server-side rendering makes it possible to plot billions of points without bandwidth constraints. Visualizations are rendered as WMS layers, or video, and can be overlaid on maps.
Mapping & Visualization API »
Time Series
Kinetica natively manages time-series data and window functions for efficient time series analysis. Powerful temporal selectors, with ASOF join filters so you can quickly gather data from the past.
Graph Analytics
Create graphs from relational data to optimize routes, match supply and demand, and uncover hidden relationships. Kinetica's flexible graph grammar has feature parity with Cypher.
Graph Analytics »
User Defined Functions
Bring your own custom algorithms or machine learning models to your geospatial data. User Defined Functions (UDFs) enable you to extend Kinetica and make inferences from those models directly alongside your data, rather than moving data out to separate systems for analysis. APIs for C++, Python & Java.
Vectorized Join Engine
Data level parallel processing unleashes significant performance improvements on many analytical functions, and particularly those for spatial and temporal queries at scale. Aggregations, predicate joins, windowing functions, graph solvers operate far more efficiently to deliver insights in real-time.
Real Time AnalysisReal-time Analysis on Large and Streaming Datasets
Kinetica was designed from the ground up to harness data level parallel processing capabilities of GPUs and modern CPUs. This unique design enables brute-force compute across huge volumes of unindexed data.
Simultaneous Ingest & Query
Kinetica's lockless, distributed architecture enables ingest at speed. And the power of vectorized queries mean there is less need to update indexes before data can be explored. This allows for truly real-time analysis on fresh data.
Tiered Storage
Get valuable context on streaming data by fusing it with data at rest. Kinetica enables you to bring fresh data into VRAM or RAM and compare it with historical data stored on disk or in external systems such as HDFS, S3 or Azure.
Vectorized Join Engine
Vectorization removes the reliance on indexes to improve performance, thereby reducing tedious data engineering and enabling real-time processing without the need for time consuming indexes to be rebuilt.
Lower TCOBetter Performance with Less Infrastructure.
With it's vectorized algorithms, dynamic tiered storage, and reduced need for supporting data structures, Kinetica enables you to do more with fewer resources and less work than comparable systems.
Large US Financial Institution
700-node spark cluster running queries in hours took seconds on 16 nodes of KineticaTop US Retailer
Consolidated 100 nodes of Cassandra(NoSQL) and Spark into 8 Kinetica nodesLarge Pharma
Identical performance between a 88-node Impala cluster and a 6-node Kinetica Intel cluster in AzureQuery, Explore, DiscoverExplore your Data, Manage your Data
Write Queries in SQL
Kinetica has broad support for SQL interaction with a mature suite of SQL functionality. This makes it easy for analysts to be productive quickly.
SQL Query Support »
Use with Popular BI Tools
Connect popular BI tools such as Tableau, Spotfire and PowerBI to Kinetica for real-time analytics using the ODBC/JDBC connector. Or try Kinetica's own Reveal BI framework
REST & Native API's
Data Scientists and Developers can develop sophisticated applications with REST and Native APIs. Language specific APIs are available for C++, C#, Java, Javascript, NodeJS & Python.
API Documentation »
Kinetica Workbench
An analytical, administrative and data management interface to Kinetica. Import data or connect to external data stores. Create workbooks for multi-step analysis, visualization and sharing of results.
Kinetica Architecture
Built to Meet the Demands of the Modern Enterprise
Scale Out
Work with petabytes of data at speed with Kinetica's memory-first, fully distributed architecture. Kinetica prioritizes and manages data across VRAM, RAM, disk, and cold storage and can create external tables for working with data in HDFS, S3 and Azure.
Role-Based Security
Kinetica secures access to data and data services by means of role-based access control. Permissions can be assigned at the table level, schema level, or globally, and can be assigned either directly or grouped into roles for assignment.
High Availability
Eliminate single points of failure and recover gracefully. Kinetica offers node and process failover for in-cluster resiliency, and multiple clusters may be grouped in a ring resiliency to spread data and ensure eventual consistency.
Administration Tools
Kinetica's administration, installation, and configuration management tools give users an intuitive way to provision cloud hardware, configure cluster security, add/remove nodes, data backup and monitor cluster health.
In the Cloud, or on Premise?
Kinetica is available as a managed service in the cloud, or can be deployed on your own hardware.
- Available as a Docker Container
- SQL Analytics
- Spatial Functions
- Graph Server/Solvers
- WMS Visualization
- UDFs
- GAdmin Interface
- Reveal BI
- All features, plus:
- Available on Azure
- Choose GPU or CPU-only
- Flexible Pricing
- Fully Managed Service
- Quick Setup
- Kafka & Azure Blob Storage
- Kinetica Workbench (NEW)
- All features, plus:
- GPU Enabled
- Cluster/Ring Resiliance HA
- Enterprise Support
- Active Analytics Workbench
- Cluster Computing
- RPM/Deb
CASE STUDY
SFEI uses drones to collect thousands of images of the San Francisco Bay, which they feed into a Kinetica-powered machine learning algorithm to detect trash - a process that has gone from days to just hours, so we can identify pollutants and take immediate action.
CASE STUDY
OVO SmartCube is designed to evaluate customer preferences using analytics powered by a machine learning-enabled recommendation engine, which makes product suggestions. Powered by Kinetica, the machine can also provide instant assessments of when and what purchases take place, in order to offer relevant deals.
Book a Demo!
Sometimes marketing copy can sound too good to be true. The best way to appreciate the possibilities that Kinetica brings to large-scale geospatial analytics is to see it in action, or try it with your own data, your own schemas and your own queries.
Contact us, and we can set you up with a demo and a trial environment for you to experience it for yourself.