Storage Optimization


Our Prediction for the Hottest Storage Category of 2009

Posted in Storage by storageoptimization on January 19, 2009
Tags: , , ,

And the winner is… dedupe for online

When it comes to storage, our market research and experience with customers have led us to the following prediction: dedupe for online storage will emerge as the hottest category of the year in 2009.

The current economic climate, coupled with the pace of advancement in cloud storage have created a perfect storm in which the need for cheap online storage is growing exponentially.

This category, which has also been referred to as “dedupe for primary” is a hot one with several entrants, one of which is my company Ocarina Networks.

Some industry observers have implied that this category is being overplayed, and that dedupe for primary won’t be as hot in the coming year as others have predicted. This is no doubt due to a misunderstanding of what is meant by “primary” storage, and where the bulk of the data growth is occurring. To clarify, we’re not talking here about dedupe for transactional databases or backups. The vast increases we’ve seen in storage demand is all in files and in nearline, not in performance-oriented primary storage.

With this in mind, here are the three key areas to consider when thinking about a dedupe solution for online:

1) How much can the product shrink an online data set with a wide mix of the typical kinds of files driving storage growth?
2) How fast can users access files that have been compressed and deduplicated?
3) How easy is it to integrate this new technology into an existing file serving environment?

I’m glad to say that Ocarina excels on all three fronts. Any product can deduplicate virtual machine images. The real question is which ones can also get good results on Exchange, Office 2007, PDF, and the wide range of image-rich data found in Web 2.0, energy, life sciences, medicine, and engineering. That’s where the rubber hits the road for our customers, and so most likely you’re going to be facing the same issues for your nearline data.

Of course, only time will tell whether this prediction is correct, but I’m betting the farm on it myself.

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