Qumulo Recognized for Distributed File Systems, Object Storage (HITS)

Qumulo has been positioned by Gartner in the Visionaries Quadrant of the 2017 Magic Quadrant for Distributed File Systems and Object Storage. The evaluation was based on Qumulo’s ability to execute and completeness of vision.

“Customers have relentlessly searched for a modern, innovative, enterprise-class file storage solution that is built for the future while also meeting today’s demanding workloads. For too long they have been forced to buy expensive legacy storage systems designed for yesterday,” said Bill Richter, CEO of Qumulo. “With Qumulo, the search is finally over and we believe the visionary positioning in Gartner’s 2017 Magic Quadrant for Distributed File Systems and Object Storage is a testament to that. The Qumulo File Fabric provides a solution that offers complete freedom — freedom to store, manage and access globally distributed sets of file data with ease and at extreme scale in the data center and in the public cloud. This is a right that all customers deserve, but one that no other vendor has been able to offer to date.”

This recognition comes on the heels of last month’s launch of Qumulo File Fabric (QF2), the world’s first universal-scale file storage system. QF2 is a modern, highly scalable file storage system, creating a single file domain that spans the data center and the public cloud. Enterprise customers now have the freedom to store, manage and access their file-based data in any operating environment, at petabyte and global scale. Hundreds of customers have already adopted QF2 for mission-critical file-based workloads. Qumulo also launched QF2 on Amazon Web Services (AWS), bringing universal-scale file storage to the public cloud.

Qumulo reports that the company has tripled sales in the first half of its fiscal year, driven by rapid customer adoption spanning data intensive industries including media and entertainment, life sciences, oil and gas, automotive, telecommunications, higher education and rapidly emerging workloads for IoT, machine learning and artificial intelligence.