Infrastructure virtualization is a proven means of streamlining hardware footprints and increasing resource agility in order to better handle the demands of burgeoning data loads and wildly divergent user requirements.
But it turns out that what is good for infrastructure is also good for data itself, which is why many organizations are looking to augment existing virtual plans with data virtualization, particularly when it comes to massive volumes found in archiving and data warehousing environments.
The Data Warehousing Institute’s David Wells offers a good overview of data virtualization and how it can drive greater enterprise flexibility. In essence, the goal is to enable access to single copies of data across disparate entities, preferably in ways that make details like location, structure and even access language irrelevant to the user. For warehousing and analytics, then, this eliminates the need to move all related data to a newly created database, which gives infrastructure and particularly networking a break because data no longer has to move from site to site in order to reach the user. Couple this with semantic optimization and in-memory caching and suddenly Big Data starts to look a lot less menacing.