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How to query billions of rows from any BI tool…in seconds

By Jonathan Greenberg | August 3, 2017

These days, practically any BI tool might claim to query billions of rows. However, I’ve yet to see any BI tool, out of the box, do this in sub-second time using SQL query…until now. As a matter of fact, in my first four months with Kinetica, we’ve been able to do this with Tableau, Power…


12 Features to Look for When Choosing a GPU-Accelerated Analytics Database

By Amit Vij | August 2, 2017

GPU acceleration is revolutionizing high-performance computing. Leveraging GPUs for processing-intensive workloads is on the rise, particularly among verticals such as finance, retail, logistics, health/pharma, and government. GPU-acceleration is opening new possibilities for machine learning, deep learning, data visualization, or simply performing faster queries, joins and row-by-row math. If you’re investigating whether a GPU database can…


How GlaxoSmithKline Uses Kinetica to Manage Their R&D Information Platform

By Richard West | July 19, 2017

Mark Ramsey, SVP, R&D Data, at GlaxoSmithKline, spoke at a recent IBM/Kinetica executive breakfast, where he discussed how GSK uses GPUs and Kinetica to help transform the way that data is used as a strategic asset within their R&D organization.   GlaxoSmithKline (GSK) is a science-led global healthcare company that researches and develops a broad…


Interactive Spatial Analysis with Massive Datasets

By James Dilworth | July 12, 2017

As more and more data becomes available from sensors, from customers, from transactions—much of it with time and location information—there are increasing demands to analyze these data sets and visualize the results on maps. But today’s geospatial toolsets are hardly up to the task. Spatial databases weren’t designed for a world where IoT systems might…


Five examples where GPU databases are bringing ‘real-time’ to IoT analytics

By Michele Nemschoff | July 7, 2017

If your organization manages large volumes of streaming IoT data, you’ll no doubt be familiar with some of the challenges of getting value and insight from these high volume, moving datasets. Many companies have turned to Hadoop and other open source technologies to store and manage these IoT data feeds. This may typically involve “duct…

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