David Noy, Veritas, sits down with Dave and Stu at Vertias Vision 2017
#VtasVision #theCUBE
https://siliconangle.com/2017/09/25/veritas-blends-its-backup-dna-into-new-big-data-ml-tools-vtasvision/
Veritas blends its backup DNA into new machine learning tools
Which technology holds the key to unlock big data’s profit potential? Veritas Technologies LLC believes that its backup legacy and new machine learning tools may be the winning combo.
“We have all this data that’s essentially sitting inactive,” said David Noy (pictured), vice president of product management at Veritas Technologies.
But the huge corpus of data that Veritas has amassed though its backup business will not produce value by itself. “Now we have products that have the capabilities — through classification engines, through engines that we’re extending, machine learning capabilities — to open that data up and actually figure out what’s inside,” Noy said.
Noy spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Veritas Vision conference in Las Vegas. (* Disclosure below.)
Within its backup corpus, there is tons of information to be gleaned from data that enterprises have amassed for years. These include containerized private clouds, Network-Attached Storage, or NAS, and on-premise or off-premise object storage.
“But, let’s face it, data is stored in a number of different other modalities,” Noy stated.
Machine learning makes light work
There are not enough human hands in organizations to pull all this data together and sift out the fool’s gold from the 24-karat real thing, according to Noy. Machine learning engines, on the other hand, are much better at quickly classifying data. Veritas has previously used such engines in its NetBackup cloud data backup and recovery suite, and the company is now expanding and improving them for further uses.
Veritas has built machine learning capabilities into new products like HyperScale for OpenStack block storage, Access scale-out NAS and Veritas Cloud Storage.
Beyond classifying data, Veritas ML capabilities can make intelligent suggestions on workloads too. “We’ve demoed at Vision [last week], machine learning capabilities to actually go and look at your workloads that are running against those underlying infrastructures and tell you [if they are] correctly positioned or not,” Noy concluded.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Veritas Vision 2017. (* Disclosure: TheCUBE is a paid media partner for Veritas Vision 2017. Neither Veritas Technologies LLC nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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David Noy, Veritas | Vertias Vision 2017
David Noy, Veritas, sits down with Dave and Stu at Vertias Vision 2017
#VtasVision #theCUBE
https://siliconangle.com/2017/09/25/veritas-blends-its-backup-dna-into-new-big-data-ml-tools-vtasvision/
Veritas blends its backup DNA into new machine learning tools
Which technology holds the key to unlock big data’s profit potential? Veritas Technologies LLC believes that its backup legacy and new machine learning tools may be the winning combo.
“We have all this data that’s essentially sitting inactive,” said David Noy (pictured), vice president of product management at Veritas Technologies.
But the huge corpus of data that Veritas has amassed though its backup business will not produce value by itself. “Now we have products that have the capabilities — through classification engines, through engines that we’re extending, machine learning capabilities — to open that data up and actually figure out what’s inside,” Noy said.
Noy spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Veritas Vision conference in Las Vegas. (* Disclosure below.)
Within its backup corpus, there is tons of information to be gleaned from data that enterprises have amassed for years. These include containerized private clouds, Network-Attached Storage, or NAS, and on-premise or off-premise object storage.
“But, let’s face it, data is stored in a number of different other modalities,” Noy stated.
Machine learning makes light work
There are not enough human hands in organizations to pull all this data together and sift out the fool’s gold from the 24-karat real thing, according to Noy. Machine learning engines, on the other hand, are much better at quickly classifying data. Veritas has previously used such engines in its NetBackup cloud data backup and recovery suite, and the company is now expanding and improving them for further uses.
Veritas has built machine learning capabilities into new products like HyperScale for OpenStack block storage, Access scale-out NAS and Veritas Cloud Storage.
Beyond classifying data, Veritas ML capabilities can make intelligent suggestions on workloads too. “We’ve demoed at Vision [last week], machine learning capabilities to actually go and look at your workloads that are running against those underlying infrastructures and tell you [if they are] correctly positioned or not,” Noy concluded.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Veritas Vision 2017. (* Disclosure: TheCUBE is a paid media partner for Veritas Vision 2017. Neither Veritas Technologies LLC nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
