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.
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.
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.
Add context, trigger alerts and make decisions by fusing streaming data with historical 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.
Join, fuse and aggregate data from streams and static datasets to create real-time representations of constantly evolving data.
Output event streams to Kafka, make decisions, and generate notifications with webhooks. Kinetica enables you to build a genuinely real-time system to monitor changing situations.
For Complex Geospatial & Temporal Questions
Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale.
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 »
Vectorized 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 »
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.
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.
User Defined Functions »
Better 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 Institution700-node spark cluster running queries in hours took seconds on 16 nodes of Kinetica
Top US RetailerConsolidated 100 nodes of Cassandra(NoSQL) and Spark into 8 Kinetica nodes
Large PharmaIdentical performance between a 88-node Impala cluster and a 6-node Kinetica Intel cluster in Azure
Explore your Data, Manage your Data
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.
REST & Native API's
API Documentation »
Evolved to Meet the Demands of the Modern Enterprise
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.
Kinetica provides advanced data access controls that let you define dynamic obfuscation, redaction, and access rules down to the column level. Kinetica works out of the box with industry standard external authentication and identity systems like LDAP, Active Directory and Kerberos.
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.
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.
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.