Blog Archives

Silos of Clusters – Is this the End Result of Data Center Modernization?

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

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New eBook: Hyperscale Performance, Is NVMe Enough?

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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DriveScale Composable Infrastructure: Elastic and Efficient Resources for Modern Workloads

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

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Is NVMe Enough for Efficient Hyperscale Data Centers?

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

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The Problems with Hyperscale Storage

Direct attached storage (DAS) is the default storage “infrastructure” for data intensive workloads like Elastic, Hadoop, Kafka and TensorFlow. The problem, as we detailed in the last blog, is using DAS creates a brittle, siloed environment. Compute nodes can’t be

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The Problems that Scale-Out Architectures Create

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

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15 Minute Webinar: Composing Infrastructure for Elastic, Hadoop, Kafka and Cassandra to Drive Down Cloud Data Center Costs

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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Simpler is Better – Solving Rack Scale Storage Problems – DriveScale Briefing Note

The storage infrastructure for multi-rack scale applications like Hadoop Spark, Cassandra, and CouchBase, are typically built using directly attached flash-based storage instead of a shared flash array. The motivation for using direct-attached storage (DAS) is simple. Media inside a server

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Using Software Composable Infrastructure for RackScale Application – DriveScale Briefing Note

Rackscale applications like Hadoop, Spark, Cassandra and others count on using commodity storage that is typically internally available to the node processing the data. The idea is to reduce storage costs and network complexity. The problem is these designs create

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Breaking Down Hadoop, Spark, Cassandra Silos – DriveScale Briefing Note

The storage architecture of most next generation applications, like Hadoop, Spark and Cassandra, leverage local, direct attached storage to avoid excessive storage traffic on the network and keep costs down. While this architecture does accomplish its goals, it also re-creates

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