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…
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.…
The public cloud is often described as “just a business model” that provides organizations with the ability to pay for storage and compute resources as they need them. If that were true then the public cloud providers could be easily…
Modern workloads such as Hadoop, Kafka and machine learning are demanding in terms of the volume of data that must be processed, the speed at which that data much be processed, and the fact that their capacity and performance requirements…
Accelerated performance and usage-based storage for business critical applications A new generation of composable infrastructure that is truly software-only is required to deliver the levels of multi-cloud data mobility, automation, efficiency and flexibility, which are required by modern workloads. These…
Hyperscale architectures typically sacrifice resource efficiency for performance by using direct attached storage instead of a shared storage solution. That lost efficiency though, means the organization is spending money on excess compute, graphics processing units (GPUs) and storage capacity that…
Most organizations now live in a multi-cloud world. Workloads can run on-premises one day and then in the cloud the next, only to return back to on-premises soon after. A challenge for organizations looking for this type of workload mobility…
Data intensive workloads like Elastic, Hadoop, Kafka and TensorFlow, are unpredictable, making it very difficult to design flexible storage architectures to support them. In most cases, scale-out architectures utilize direct attached storage (DAS). While DAS delivers excellent performance to the…
Hyperscale applications like Elastic, Hadoop, Kafka and Cassandra typically use a shared nothing design where each node in the compute cluster operates on its data. Hyperscale architectures, to maximize storage IO performance, keep data local to the compute node processing…
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