Metanautix Looks to Simplify Big Data Access and Management

Mike Vizard
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Four Steps to Ensure Your Big Data Investment Pays Off

The biggest challenge with Big Data these days isn’t so much the size of it as it is providing access to it. Most data is strewn across multiple repositories in a way that makes accessing it cumbersome and slow, no matter how fast the platforms that data resides on actually are.

To address that issue, Metanautix announced today it has picked up an additional $7 million in funding to develop a set of data engines that leverage standard SQL to provide business analysts access to data where it resides in a way that is much simpler and faster.

Founded by engineers from Google and Facebook, Metanautix CEO Theo Vassilakis says rather than cobbling together a variety of legacy technologies to access data, Metanautix is building an end-to-end platform for managing the data supply chain that makes it possible for the average analyst to not only access data, but also move it wherever they want.

Vassilakis says the platform that Menanautix is developing is essentially the commercialization of a type of data analytics platform that has already been built by engineers at Facebook and Google. Instead of having to manually build that platform by hand, Metanautix is dedicated to the proposition that the average enterprise IT organization would rather license software that is supported by a vendor.


Going forward, Vassilakis says the biggest issue facing IT organizations may not be the size of the data that needs to be managed as much as all the variants of types of data there now are. For instance, being able to analyze audio and video data alongside other forms of structured and unstructured data is a major challenge.

While data warehouses will continue to grow, Vassilakis says the sophistication of the tools being given to analysts to both access and manage all that data needs to substantially improve.

Obviously, Metanautix isn’t going to be the only startup or existing vendor looking to solve this Big Data problem. The good news is that a lot of money, in one form of another, is finally being allocated to solve the problem.



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