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…
The StorageSwiss Report is a weekly discussion about hot trends and topics going on in the storage, cloud, and data protection markets. We don’t just cut & paste press releases. We provide insight as to why or why not the…
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…
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…
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…
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…
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…
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.…
Workloads like artificial intelligence (AI), machine learning (ML), big data analytics, the Internet of Things (IoT) and data warehousing need storage memory-levels of performance. Charles Fan, CEO of MemVerge, has a vision for making this possible without disrupting the application…
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