SolidFire in Partnership With MongoDB

SolidFire, Inc., a provider of SSD storage systems designed for data centers, announced an expanded presence in the database market with a partnership with MongoDB, Inc.

The storage solution combines dynamic performance control with simplicity and scalability delivered in a MongoDB. environment.

“The MongoDB ecosystem is expanding rapidly, the scale-out approach to storage from SolidFire is ideal for our users,” said Matt Asay, VP of business development and corporate strateg, MongoDB. “With SolidFire, our customers have additional control over database performance and the ability to adjust storage performance resources on-demand.”

While service providers evolve from dedicated hardware models to a shared cloud infrastructure and enterprise customers embrace the next era of big data, optimizing the database for scale and guaranteed performance becomes critical to success. By leveraging SSD technology with concepts like storage virtualization, QoS and horizontal scaling, SolidFire’s storage systems are able to combine the comforts of traditional dedicated storage performance with the simplicity and scalability expected in a MongoDB environment.

“One of the major restrictions in large scale database deployments is lack of infrastructure flexibility imposed by running on dedicated hardware,” said Dave Cahill, director of strategic alliances, SolidFire. “With SolidFire, running your databases on dedicated hardware is no longer your only option. MongoDB with SolidFire, delivers new IT efficiencies and operational cost savings along with consistent, repeatable IO performance in a highly scalable, extremely flexible database environment.”

SolidFire will host with MongoDB Tuning MongoDB for Next Generation Storage Systems webinar on November 6th.

Chris Merz, Sr. database application engineer, SolidFire, will discuss how to:

• Architect MongoDB with SolidFire storage for a large scale production cloud environment.
• Traverse the technology stack to identify performance bottlenecks.
• Optimize IO performance and latency.
• Normalize performance under load.
• Maintain performance at scale.