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Self-driving cars? Get ready for self-driving data

Lance Smith | June 16, 2015
Once the dreams of science-fiction, self-driving cars will soon allow passengers to specify a destination and let the car pick the best route based on factors such as time, traffic, freeways and fuel consumption. This kind of automation for an enterprise's most precious commodity -- data -- is also soon coming to a data center near you.

One might think that advances in modern data center technology would be making storage simpler. In fact, the opposite is true. The growth and diversity of today's data is making data center management more complex. For example, supporting today's real-time business analytics, sub-millisecond transactional response times, and potentially massive and unpredictable workload spikes require higher performance and lower latencies than shared storage can cost-effectively support. Consequently, many organizations are implementing shared-nothing architectures for mission-critical applications that place data on flash in a server, creating an entirely new (yet faster) silo.

Another example is cloud computing, which promises plenty of inexpensive capacity to help enterprises reduce costs, flexible access to resources to increase agility, and edge locations that enable globally distributed workforces. But cloud management tools don't offer the same visibility and control as on-premise tools. In addition, they don't integrate well with existing management software, making the cloud yet another new silo to contend with.

Intelligent data mobility

The ideal solution to the problem of data silos would give enterprises comprehensive visibility into all of their data, while transparently automating data movement by policy. In fact, new technologies are now leveraging data virtualization to offer just such a solution.

Data virtualization abstracts an application's logical view of data from the underlying storage hardware within a global data space, allowing enterprises to see and access all of their data from a single pane of glass. The diagram below illustrates one approach to data virtualization:

In this architecture, the metadata, which includes information about the data, is separated from the actual data. Applications are able to look up the location of the data from the metadata server, and then access the data directly, instead of accessing a dedicated storage device that only has information about data stored on that particular device. This is similar to the way DNS servers are used to translate "Google.com" into the physical location of a server hosting the web page.

Once data is virtualized, it can live anywhere, allowing policy-driven, intelligent data mobility to automatically move data to the right storage as business needs evolve and without application interruption. Rather than having to add yet another new type of storage to introduce data virtualization to an enterprise's infrastructure, storage agnostic solutions enable existing storage to be used at maximum efficiency.

Automatically place data on the right resource

By automating many complex storage management tasks, enterprises can finally stop managing storage and start managing data. IT professionals can stop spending time sizing storage capacity and performance based on highly educated guesses regarding the long-term needs of applications. With data virtualization, they can finally get ahead of the data snowball by creating policies that specify Service Level Objectives (SLO) to best meet their customers' needs, leveraging software to automatically place data on the best resource to meet these objectives.

 

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