Blog Archives

Understanding the Problem of Unification in Most Software-Defined Storage Solutions

The promise of software-defined storage (SDS) aims to unify all of your organization’s storage assets through a single user interface. But that interface is supposed to do more than provide a single pane of glass for monitoring. It is also

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Posted in Blog

Does All-Flash Kill Data Management?

Most of today’s modern storage systems have a scale-out design, meaning they can expand to meet almost all of the data center’s capacity requirements. Also, since an increasing number of these systems are all-flash, they can meet most data center’s

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Can Current Storage Infrastructure Meet the AI at Scale Demand?

In our last blog we covered the challenges that AI at scale creates for storage infrastructures. To support the coming wave of AI applications, storage infrastructures need to deliver a tremendous amount of storage capacity with the ability to retain

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Complexity is Killing Secondary Storage

Most organizations only use their secondary storage infrastructure as protection storage. In some cases, this is because the secondary storage hardware only supports the backup use case. Even if the organization selects more flexible secondary storage hardware, it still primarily

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Posted in Briefing Note

Understanding the Challenges That AI at Scale Creates

Artificial Intelligence (AI) is in its infancy and the requirements it places on the storage architectures that support these workloads are not widely understood. As a result, an organization starting an AI initiative can initially get away with using an

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The How and Why of High-Performance Primary Storage as-a-service

The key to reducing primary storage costs is to better manage the data that is on primary storage. The problem remains that IT lacks the tools, and more importantly the time, to properly manage the workloads that primary storage supports.

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Posted in Article

Is Your Storage Architecture Ready for the Coming AI Wave?

Artificial Intelligence (AI) is a broad term that can apply to various computing tasks, including machine learning, deep learning, and big data analytics. Many AI projects are in a proof of concept stage, but CIOs and IT Managers need to

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