Why Flash Proves to be Cheaper and Faster for Big Data

Data is driving the world in ways many could only have imagined a few years ago. Detailed information about nearly every aspect of life is being generated at an unprecedented pace, with some estimates even showing that the amount of data generated doubles every two years.

Simply put, thats a lot of numbers that need to be collected, sorted, analyzed, and applied to whatever solutions organizations are pursuing. Finding the best way to adopt big data analytics in an affordable and effective way has been the challenge for many businesses.

For years, the use of RAM for high-speed data processing has been the default choice for companies, in part because it was cheap and could handle the workload. But as data grows around the world, RAM has struggled to keep up with demand. Now many are starting to turn to flash as the means by which data can be processed and used more effectively.

Much of the optimism surrounding NAND flash comes from a recent study at MIT that compared the capabilities of RAM and flash when it comes to big data. In that study, researchers were able to build a server network that showed flash could get the job of data processing done just as quickly as RAM.

The basic study worked by making a network of 20 flash-based servers process on the same level as 40 servers based on RAM. The overall result was not only an equivalent speed between the two, but more efficiency from the flash side of things. To be able to process data on such a scale at such a high speed while still maintaining impressive efficiency is the very breakthrough many businesses have been waiting for.

The appeal of the results of this research go beyond the speed at which big data can be analyzed. Also of note is the lower costs associated with using flash. This hasnt always been the case, of course. While few would dispute the processing capabilities of flash storage years ago, many organizations still wouldnt use it because flash was simply more expensive.

The high cost represented a formidable barrier preventing smaller businesses from truly taking advantage of big data analytics. The cost of flash, however, has declined significantly in just the past few years, making it far more competitive with hard disk drives and RAM. The researchers at MIT were able to use NAND flash for their study, an option that is ten times cheaper than RAM. Coupled with this less expensive option, researchers also didnt need as much in terms of hardware numbers. The combination of cheaper hardware and less equipment means flash is proving to be the cheaper option.

The savings that come from using NAND flash for big data dont end there. One notable benefit of flash storage is the lower energy consumption when compared to RAM. In small doses, the difference may not be significant, but when used for processing big data, the gap between the two becomes wide. As has proven to be the case, flash uses a lot less energy than more traditional options. That means cost savings come two-fold — from the cheaper hardware and the savings found on the energy bill when all is said and done.

The importance of these developments is difficult to understate. The capabilities provided by using NAND flash (both cost wise and performance wise) are significant in that it opens up new possibilities for businesses willing to use it. Of particular note is the use of flash for real-time analytics.

Processing massive amounts of data in real-time has become tantamount to business success as organizations seek to analyze information the moment it is generated. Companies want answers on how to act on data as quickly as possible, and the use of flash storage makes this an attainable goal.

The rise of big data means large data sets are now commonplace, requiring the right technology to process it. Beyond that, it has to be affordable for most organizations to use. Flash meets these demands, helping businesses use big data and increasing their chances of success. As the price of flash comes down and more companies understand how to use it, flash should become a prominent feature of any big data operation.