George A. Crump

Why Storage Recovery Architectures Matter More Than Drive Reliability Enterprise storage discussions still spend most of their time on drive reliability. Buyers compare enterprise SSDs against consumer SSDs, new media against refurbished, endurance ratings, firmware revisions, warranty terms, and vendor qualification programs. Those conversations have merit. Drives fail, and the quality of the…

Containers disappear by design. The workload does not. That distinction sits at the center of nearly every Kubernetes recovery failure. Development teams describe containers as ephemeral, and from the perspective of the container image and pod lifecycle, they are correct. The image is immutable. The pod is disposable. Kubernetes restarts failed instances and reschedules workloads across the…

Kubernetes was originally optimized for ephemeral and stateless workloads. Containers would spin up, process requests, and disappear without retaining state between invocations. Enterprise adoption changed that model. Databases, analytics platforms, message queues, AI pipelines, and stateful application services moved into Kubernetes clusters, and persistent storage became a core infrastructure…

Conventional DRAM contract prices rose 90 to 95 percent quarter-over-quarter in Q1 2026, and TrendForce projects another 58 to 63 percent on top in Q2. The memory market broke in 2025, and the assumption that prices will normalize on the usual cycle is wrong. DRAM and NAND flash are not in a temporary shortage. They are in a structural reallocation that will hold prices elevated through 2027 and…

The recovery time gap is not a backup product problem. Backblaze’s 2024 data shows only 35 percent of organizations that expect to recover within hours actually do. The 65 percent who miss their target are not running bad backup products. They are running bad recovery architecture. Key Takeaways Only 35 percent of organizations that expect to recover within hours actually do (Backblaze 2024).…

Hypervisor deduplication has a storage efficiency problem that most buyers don’t discover until after deployment. For years, the overhead it creates was hiding in plain sight — organizations focused on RAM consumption, licensing costs, and hardware compatibility while storage overhead quietly consumed capacity that was never accounted for. In 2026, with enterprise SSD pricing up 472 percent year…

The memory crisis was supposed to be IT’s problem — not Broadcom’s. Broadcom’s VMware pricing changes pushed IT budgets to the breaking point in 2024 and 2025. The financial case for leaving was clear, and organizations across every sector began evaluating alternatives. Then the memory market collapsed, and the cost to exit VMware in many cases exceeded the cost of staying. Broadcom did not…

Are you ready for the three-generation disaster stack? Most backup architectures are built for one generation of risk, sometimes two. The organizations making infrastructure decisions right now need to cover all three simultaneously, and the choices they make on hypervisor and backup platform directly determine whether they can. Rick Vanover, VP of Product Strategy at Veeam, laid out this…

Every new hypervisor platform claims enterprise readiness. The oVirt API is how backup vendors verify that claim. The VMware alternative market has a fragmentation problem. Dozens of KVM-based platforms now compete for enterprise workloads. Each one ships its own management layer, its own storage model, its own API surface. Backup vendors face an impossible math problem: build and maintain a…

Every AI project follows the same arc. A data science team identifies a use case, connects a large language model to internal documents, builds a retrieval pipeline, and produces a working demo. The prototype runs on a developer’s workstation or a cloud notebook with a GPU allocation. It works and demos well. Then it collides with the reality of AI production infrastructure. Then the conversation…

Every GPU virtualization conversation eventually hits the same fork in the road: do you give a virtual machine the entire GPU, share it across multiple VMs, or carve it into hardware-isolated slices? The answer depends on your workload, your budget, and how much isolation you actually need. Getting it wrong costs you either performance or money, and often both. Key Takeaways GPU virtualization…