Review: OmniSci GPU database lifts huge data sets

GPU acceleration of database, rendering, and visualization enables interactive exploration of data sets with billions of rows

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At a Glance

Many of us are awash in data, to the point where conventional databases and conventional BI systems can’t keep up, at least not in real time. There are workarounds, such as sampling the data or working with day-old reports, but each one is a compromise.

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OmniSci, formerly called MapD, can keep up with massive amounts of data in real-time, by using GPUs to accelerate its database, rendering engine, and visualization system. OmniSci has found applications in a number of industries that generate significant amounts of data, including telecom, automotive telematics, oil and gas exploration, defense, and intelligence.

With both mapping and BI capabilities and sub-second response times even with tens of millions of rows, you would expect OmniSci to compete directly with Tableau and Esri. But in fact OmniSci makes a big deal about how it can be used to accelerate both Tableau and Esri.

According to the company, OmniSci will be integrated with machine learning capabilities and become more interesting to data scientists in the next year. That makes technological sense, since the product already depends on CUDA and Nvidia GPUs, and since Nvidia has developed the necessary GPU-accelerated machine learning and deep learning libraries. I’m not clear, however, on how that will work from the viewpoint of a user.

Alternatives to OmniSci as a GPU-accelerated database analytics platform include Brytlyt, SQream DB, BlazingSQL, and Kinetica.

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