Most businesses’ workload ecosystems are in a state of transition. There is plenty of buzz about modern workloads such as artificial intelligence (AI), machine learning (ML), high-velocity analytics and NoSQL databases as new tools to drive competitive advantage. The reality,…
Most storage consolidation strategies fail because they attempt to consolidate to a single piece of storage hardware. To successfully consolidate storage, IT professionals need to look at consolidation strategies that worked. Server consolidation was VMware’s first use case. It was…
Data management is moving to the top of many IT project whiteboards and the reason why is simple, organizations are drowning in unstructured data. There is too much data, too many files and organizations store it in too many locations.…
Legacy storage network architectures can’t keep up with the scale and performance requirements of modern data sets and workloads such as the Internet of Things (IoT) analytics, creating the need for transformation to support new business demands. Fibre Channel is…
A web-scale data center needs to scale to support thousands if not millions of users. Web-scale application use cases range from traditional transactional IT to modern data analytics and machine learning workloads. It also includes new advanced video applications and…
Organizations are almost universally struggling with storing and managing their unstructured data. From a storage system perspective, vendors offer legacy NAS systems, object storage systems and cloud storage services, all trying to convince IT professionals that they are the best…
Artificial Intelligence (AI) and Machine Learning (ML) are becoming mainstream initiatives at many organizations. Data is at the heart of AI and ML. Immediate access to large data sets is pivotal to successful ML outcomes. Without data, there is no…
Most Artificial Intelligence (AI) projects start off as a skunkworks project that when ready for production is tossed over the wall for IT to handle. In many cases, IT tries to store the AI workload on an existing network attached…
Analytics is evolving from big data, machine learning to artificial intelligence. Machine learning is the analysis of data at rest, artificial intelligence (AI) is the analysis of data in real-time. Machine learning is predictive; AI is cognitive. The requirements of…
Artificial Intelligence (AI) and Machine Learning (ML) are becoming mainstream initiatives at many organizations. Data is at the heart of AI and ML. Immediate access to large data sets is pivotal to successful ML outcomes. Without data, there is no…
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