Stop counting megabytes; it’s all about application-aware storage now

An IT industry analyst article published by SearchStorage.

Raw capacity numbers are becoming less useful as deduplication, compression and application-aware storage provide more value than sheer capacity.

Whether clay pots, wooden barrels or storage arrays, vendors have always touted how much their wares can reliably store. And invariably, the bigger the vessel, the more impressive and costly it is, both to acquire and manage. The preoccupation with size as a measure of success implies that we should judge and compare offerings on sheer volume. But today, the relationship between physical storage media capacity and the effective value of the data “services” it delivers has become much more virtual and cloudy. No longer does a megabyte of effective storage mean a megabyte of real storage.

Most array vendors now incorporate capacity-optimizing features such as thin provisioning, compression and data deduplication. But now it looks like those vendors might just be selling you megabytes of data that aren’t really there. I agree that it’s the effective storage and resulting cost efficiency that counts, not what goes on under the hood or whether the actual on-media bits are virtual, compacted or shared. The type of engine and the gallons in the tank are interesting, but it’s the speed and distance you can go that matter.

Corporate data that includes such varied things as customer behavior logs, virtual machine images and corporate email that’s been globally deduped and compressed might deflate to a twentieth or less of its former glory. So when a newfangled flash array only has 10 TB of actual solid-state drives, but based on an expected minimum dedupe ratio is sold as a much larger effective 100+ TB, are we still impressed with the bigger number? We know our raw data is inherently “inflated” with too many copies and too little sharing. It should have always been stored “more” optimally.

But can we believe that bigger number? What’s hard to know, although perhaps it’s what we should be focusing on, is the reduction ratio we’ll get with our particular data set, as deflation depends highly on both the dedupe algorithm and the content…

…(read the complete as-published article there)