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 modern workload set has fundamentally changed the nature of and also how we handle data sets. Previously, critical business applications were typically driven by the processing of large, monolithic databases that were comprised of large files. Today, more and…
The data center is a series of networks. There are networks that connect servers to users and networks that connect storage to servers. Even within servers and storage there are networks to connect CPU to IO devices like flash drives…
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…
NVMe (Non-Volatile Memory Express) flash drives thrust storage media from the position of the worst performing component of the data center to the best. The technology’s low latency however, exposes other bottlenecks that went previously undetected. The new storage performance…
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…
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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