For analytics support, all-flash arrays are an increasingly popular option

All-flash arrays (AFAs) are a logical choice for high-performance applications, says one expert, as they outperform traditional HDDs and provide more reliable and efficient storage.

Jeff Rowe | May 04, 2018 12:00 am

Data volumes are growing rapidly, in healthcare and other sectors, and the more data is generated the more stakeholders want to mine the data for an array of insights.

As a result, data scientists are turning to all-flash storage that is designed to help them target analytics workloads. According to tech consultant Robert Sheldon, the advantages that all-flash storage offers analytics come from recent advances in flash technologies.

For example, he notes, “the development of triple-level cell (TLC) storage has been critical in boosting flash capabilities. This solid-state NAND flash memory stores three bits of data per cell, making it less expensive than single-level cell and multi-level cell flash. And 3D NAND flash, which stacks memory cell layers on top of one another to provide higher densities, when combined with TLC technologies, has resulted in large increases in storage capacities and performance. TLC and 3D NAND have switched the focus away from endurance and toward capacity and performance -- a boon to analytics.”

He adds that AFA storage has also benefitted from the introduction of “nonvolatile memory express (NVMe), a host controller interface and storage protocol that accelerates data transfers between servers and SSDs.”

Taken together, “these technologies make it possible for AFAs to support sophisticated analytics in a way not possible before.”

The net result is that all-flash arrays “can be as much as 10 times faster than HDDs, while boasting a latency rate of less than a millisecond. With no moving parts, AFA storage is also more efficient and reliable than HDDs.”

Moreover, analytics workloads tend to require fewer servers when using AFAs, because they support higher capacities. This, along with being faster and having lower latencies, make them well-suited to the data-intensive processing of analytics workloads.

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