Availability on AWS Marketplace coincides with launch of Matrix Version 3.1, which delivers support for snapshots to to Amazon Simple Storage Service (Amazon S3)
SAN JOSE, Calif. – Nov. 27, 2017 – WekaIO, a leader in high performance, scalable file storage for data intensive applications announced the latest version of its cloud-based scalable file system, WekaIO Version 3.1, which delivers an enterprise-class, feature-rich, HPC storage for compute-intensive applications and technical compute workloads. The new snapshot functionality in Matrix Version 3.1 is designed to help users fully realize the promise of the cloud with a single vendor solution to provide remote backup to Amazon S3, provision resources for cloud bursting workflows or utilize a Pause/Resume feature to underpin a hybrid cloud strategy for cost-effective data archiving.
With Amazon S3, Matrix Version 3.1 supports cloud bursting, allowing users with on-premises compute clusters to elastically grow their environment in response to peak workload periods, or move their entire high performance workloads to the cloud. The snapshot to the Amazon S3 functionality simplifies periodic backup, delivering a single-vendor backup solution, whereby an entire file system and its data can be backed up on Amazon S3, without the need to purchase additional backup tools. The remote cloud copy can be automatically updated as frequently as desired, without impacting application performance. It also can serve as an easy way to perform a “fire-drill” audit of the backup by simply rehydrating the file system on Amazon Web Services (AWS) as a test copy.
“There continues to be a strong trend within IT and research organizations to adopt a cloud strategy to accommodate the demands of high-performance applications,” said Sabina Joseph, Head of Global Storage Partnerships & Alliances at Amazon Web Services, Inc. “WekaIO has already demonstrated that high performance workloads can achieve outstanding performance on AWS through its SPEC SFS 2014 results. Leveraging AWS, customers can now benefit from a solution developed specifically to scale their businesses, making it easier to move high-performance applications to the cloud.”
WekaIO is fully self-service and provisionable on most Amazon Elastic Compute Cloud (Amazon EC2) instances with locally attached SSDs, and is ideal for analytics, genomics and HPC customers looking for a feature-rich, high-performance file system for their compute intensive applications running on AWS. The software can be automatically provisioned as a hyperconverged solution where applications and storage share the same instances, or as dedicated storage servers that connect other lower cost Amazon EC2 instances without local SSD storage.
Matrix Version 3.1 with snapshot to Amazon S3 functionality incorporates a Pause/Resume feature that is an ideal cost-effective cloud bursting and data archive strategy. Users may run Matrix Version 3.1 on Amazon EC2 instances for high application performance, stop instances, and pay only for cost-effective Amazon S3 storage, then resume future computations by re-spinning the Amazon EC2 instances.
“Using WekaIO Matrix Version 3.1 in conjunction with AWS allows enterprises to develop a true utility compute and storage model,” said Omri Palmon, Co-founder and Chief Product Officer at WekaIO. “The combined solution is designed to deliver extreme performance, high bandwidth and cloud scalability while delivering significant cost savings in CAPEX and OPEX.”
WekaIO will be demonstrating Matrix Version 3.1 at Booth #507 at AWS re:Invent 2017 in Las Vegas on Nov 27-Dec 1.
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WekaIO Version 3.1 is available now on AWS Marketplace and through select resellers.
WekaIO leapfrogs legacy infrastructures and improves IT agility by delivering faster access to data with software-centric data storage solutions that accelerates application performance and unlocks the true promise of the cloud. WekaIO Matrix™ software is ideally suited for performance intensive workloads such as Web 2.0 application serving, financial modeling, life sciences research, media rendering, Big Data analytics, log management and government or university research.
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