As analytics environments like Hadoop, Elastic, Kafka and TensorFlow continue to scale, organizations need to find a way to create a shared infrastructure that can deliver the bandwidth, flexibility, and efficiency that these environments need. In a recent Storage Intensity…
Artificial Intelligence (AI) and Machine Learning (ML) workloads are fundamentally different from any other workload. These workloads deal in data sets measured in dozens of petabytes of capacity, and large percentages of it can go from dormant to active at…
The modern data center is increasingly microservice or container-based. These workloads are dynamic and unpredictable. The datasets within these workloads range from thousands of large files to billions of small files. Artificial Intelligence (AI) and Machine Learning (ML) are being…
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) projects can all have varying types of data associated with them. Some projects consist of a relatively small number of huge files, and others have an incredibly high number of…
The data center, in most cases, is a mixture of legacy and modern applications that exist, both on-premises and in the cloud. To provide organizations with a competitive advantage, IT needs to use and manage these resources efficiently. IT professionals…
Ransomware gets a lot of attention from IT professionals and data protection vendors alike. Ransomware however, is only one door that bad actors can use to compromise the data center. Another door is application or operating system vulnerability. While several…
Network Attached Storage (NAS) systems were once the primary storage destination for all unstructured data but with file-counts soaring past one billion and with machines replacing users as the primary creators of data, the industry is seemingly moving past NAS…
Accelerating a data analytics project is critical for storage vendors that sell storage systems designed for big data analytics, artificial intelligence (AI), and machine learning (ML). These vendors all thrive by helping their customer get answers to their questions faster…
Applications like Elastic, Hadoop, Kafka and TensorFlow typically operate on scale-out architectures built from dozens, if not hundreds of servers, which act as nodes in the application’s cluster. Many organizations now use a mix of these applications to derive the…
Hyperscale architectures that support Elastic, Hadoop, and Kafka, often vary wildly between organizations and even within each organization. Each workload often needs its own cluster and IT teams are constantly trying new technology within those clusters, trying to improve performance.…
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