Review: Kinetica analyzes billions of rows in real time

GPU database is not only hugely scalable, but integrates graph analysis, location intelligence, and machine learning with standard SQL

At a Glance

In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica.

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The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.

As reference customers, Kinetica has, among others, Ovo, GSK, SoftBank, Telkomsel, Scotiabank, and Caesars. Ovo uses Kinetica for retail personalization. Telkomsel, the Indonesian wireless carrier, uses Kinetica for network and subscriber insights. Anadarko, recently acquired by Chevron, uses Kinetica to speed up oil basin analysis to the point where the company doesn’t need to downsample its 90-billion-row survey data sets for 3D visualization and analysis.

Kinetica is often compared to other GPU databases, such as OmniSci, Brytlyt, SQream DB, and BlazingDB. According to the company, however, they usually compete with a much wider range of solutions, from bespoke SMACK (Spark, Mesos, Akka, Cassandra, and Kafka) stack solutions to the more traditional distributed data processing and data warehousing platforms.

Kinetica key features and architecture

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