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Beyond the Hype: The Truth behind the Marketing Claims of Many New Data Storage Platforms

Pure Storage

In our opinion, a true “modern data storage platform” can consolidate fragmented data silos into a seamless, simple, and efficient system that’s standards-based to meet the evolving workload requirements of today and tomorrow. Evaluating Your Choices We talked about the growing storage choices now available.

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Avoid Tricks and Treat Yourself to Energy-efficient Storage

Pure Storage

Avoid Tricks and Treat Yourself to Energy-efficient Storage by Pure Storage Blog Summary While a storage system may appear to be an energy-efficient solution in a benchmark, the actual performance may differ when it’s scaled to meet the specific needs of a business.

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Lewin’s 3 Stage Model of Change Explained

BMC

Any interaction or force affecting the group structure also affects the individuals behavior and capacity to change. To understand group behavior, and hence the behavior of individual group members during the change process, we must evaluate the totality and complexity of the field.

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Understand resiliency patterns and trade-offs to architect efficiently in the cloud

AWS Disaster Recovery

Firms designing for resilience on cloud often need to evaluate multiple factors before they can decide the most optimal architecture for their workloads. Before you decide to implement higher resilience, evaluate your operational competency to confirm you have the required level of process maturity and skillsets. Trade-offs.

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The SSD Trap: How a Storage Solution’s Reliance on SSDs Can Impact You (Part 1 of 2)

Pure Storage

As more enterprises prioritize sustainability as a key criteria for new AFA purchases, metrics like energy efficiency (TB/watt) and storage density (TB/U) become critical in evaluating the cost of new systems. are needed to build a system to meet any given performance and capacity requirements. Next, let’s look at DRAM.

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How to Take Your AI into Production without Breaking the Bank

Pure Storage

Beware: Existing data center infrastructure and cloud resources may not meet the higher performance, scale, and/or availability requirements of production-level AI. It needs a data scientist involved to continually evaluate model performance—which can degrade more rapidly than conventional software.

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Why Does Storage Matter?

Pure Storage

Cleaning, where the raw data is sorted, evaluated, and prepared for transfer and storage. . Finally, a portion of the data is held back to evaluate model accuracy. For legacy storage systems, this is an impossible design point to meet, forcing the data architects to introduce complexity that slows down the pace of development.