Microsoft acquires Metanautix for in-house data analytics

Microsoft has announced that it has acquired Metanautix, a startup company that was developing its Quest data compute engine which aims to help large enterprises filter through their vast amounts of in-house data.

Metanautix, which was co-founded in 2012 by Theo Vassilakis and Toli Lerios, was launched in September 2014 after raising $7 million of funding after Quest had been two years in development.

“Metanautix started out with the vision to integrate the data supply chain by building the Quest data compute engine that enables scalable SQL access to any data,” said Vassilakis. “Three years in, we can take this work to the next level by joining forces with Microsoft. We look forward to being part of Microsoft’s important efforts with Azure and SQL Server to give enterprise customers a unified view of all of their data across cloud and on-premises systems.”

Microsoft announced the acquisition by describing Metanautix as an innovative company helping to solve one of the biggest challenges in the world of data analytics: How can a company bring all of its data together for analytics to gain powerful insights, discover new opportunities and drive growth through intelligent automation?

The problem with data analytics is that companies store vast quantities of data but they spread it across many databases and stores, meaning they can only run analytics on a fraction of the data. What Quest sets out to do is to allow large companies to connect all those data sources and silos, so that analytics can be performed on the whole of the data regardless of type, size or location.

Microsoft described Quest as: “a solution that can integrate data across traditional data warehouses like SQL Server, Oracle and Teradata; open source NoSQL databases such as MongoDB and Cassandra; as well as business systems like Salesforce.com and wide array of other cloud and on-premises data stores.

"Key to Metanautix’ approach is making a wide variety of data query-able by SQL, the most widely used data query language – at speed and high scale.”

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