May 7, 2009 – As mentioned in a previous post (see “Welcome to the data dedupalooza”
), I’ve been researching the topic of data reduction for primary storage for an upcoming article. One thing I’ve noticed is that the industry is playing fast and loose with the term ‘data de-duplication,’ sometimes using it in place of the more general ‘data reduction’ or ‘capacity optimization’ terms.
Maybe that was ok in the realm of secondary storage, but as you begin considering data reduction for primary storage you’ll want to be more specific about what technology a vendor uses, although all of the technologies produce the same result (to varying degrees): reduced storage capacity and costs.
In the secondary storage space, in addition to data de-duplication, some vendors use single instancing or compression or a combination of techniques. In the primary storage space, some vendors use data de-duplication, some use compression only, and some combine the two.
Again, all of these technologies have the same goal, but in the case of primary storage (and depending on the type of data sets you have), it may make a difference how a vendor is implementing data reduction. And it’s misleading to refer to any
type of data reduction as ‘data de-duplication,’ despite the popularity of the term.
If you’re interested in data reduction (aka
capacity optimization) to reduce your capacity requirements, delay new purchases, and slash power, cooling and space requirements, plan to attend our upcoming Webcast, titled “Leveraging Capacity Optimization to Reduce Datacenter Footprint and Storage Costs," Tuesday, May 12 at 10:00 PST, 1:00 EST. Presenters will include Noemi Greyzdorf, research manager, storage software, at IDC, and Peter Smails, senior vice president of worldwide marketing at Storwize.
The Webcast will cover data reduction for all types of storage -- including primary, nearline and secondary data – and provide tips on how to maximize the benefits of capacity optimization. To register, click here.