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Archiving the Big Data Old Tail
At any point in time, half of your Big Data are more than two years old
By: Jason Bloomberg
Feb. 28, 2013 08:00 AM
Scenario #1: out of the blue, your boss calls, looking for some long-forgotten entry in a spreadsheet from 1989. Where do you look? Or consider scenario #2: said boss calls again, only this time she wants you to analyze customer purchasing behavior...going back to 1980. Similar problem, only instead of finding a single datum, you must find years of ancient information and prepare it for analysis with a modern business intelligence tool.
The answer, of course, is archiving. Fortunately, you (or your predecessor, or predecessor's predecessor) have been archiving important-or potentially important-corporate data since your organization first started using computers back in the 1960s. So all you have to do to keep your boss happy is find the appropriate archives, recover the necessary data, and you're good to go, right?
Not so fast. There are a number of gotchas to this story, some more obvious than others. Cloud to the rescue? Perhaps, but many archiving challenges remain, and the Cloud actually introduces some new speed bumps as well. Now factor in Big Data. Sure, Big Data are big, so archiving Big Data requires a big archive. Lucky you-vendors have already been knocking on your door peddling Big Data archiving solutions. Now can you finally breathe easy? Maybe, maybe not. Here's why.
Archiving: The Long View
Cloud to the Rescue?
At some point the answer may be yes, but Cloud Computing is still far too immature to jump to such a conclusion. Will your CSP still be in business decades from now? As the CSP market undergoes its inevitable consolidation phase, will the new CSP who bought out your old CSP handle your archive properly? Only time will tell.
But even if the CSPs rise to the archiving challenge, you may still have the file format challenge. Sure, archiving those old Lotus 123 files in the Cloud is a piece of cake, but that doesn't mean that your CSP will return them in Excel version 21.3 format ten years hence-an unfortunate and unintentional example of garbage in the Cloud.
The Big Data Old Tail
The point to Big Data is that the indicated data sets continue to grow in size on an ongoing basis, continually pushing the limits of existing technology. The more capacity available for storage and processing, the larger the data sets we end up with. In other words, Big Data are by definition a moving target.
One familiar estimate states that the quantity of data in the world doubles every two years. Your organization's Big Data may grow somewhat faster or slower than this convenient benchmark, but in any case, the point is that Big Data growth is exponential. So, taking the two-year doubling factor as a rule of thumb, we can safely say that at any point in time, half of your Big Data are less than two years old, while the other half of your Big Data are more than two years old. And of course, this ZapFlash is concerned with the older half.
The Big Data archiving challenge, therefore, is breaking down the more-than-two-years-old Big Data sets. Remember that this two-year window is true at any point in time. Thinking about the problem mathematically, then, you can conclude that a quarter of your Big Data are more than four years old, an eighth are more than six years old, etc.
Combine this math with the lesson of the first part of this ZapFlash, and a critical point emerges: byte for byte, the cost of maintaining usable archives increases the older those archives become. And yet, the relative size of those archives is vanishingly small relative to today's and tomorrow's Big Data. Furthermore, this problem will only get worse over time, because the size of the Old Tail continues to grow exponentially.
We call this Big Data archiving problem the Big Data Old Tail. Similar to the Long Tail argument, which focuses on the value inherent in summing up the Long Tail of customer demand for niche products, the Big Data Old Tail focuses on the costs inherent in maintaining archives of increasingly small, yet increasingly costly data as we struggle to deal with older and older information. True, perhaps the fact that the Old Tail data sets from a particular time period are small will compensate for the fact that they are costly to archive, but remember that the Old Tail continues to grow over time. Unless we deal with the Old Tail, it threatens to overwhelm us.
The ZapThink Take
Fair enough. But there are perhaps far more examples of Big Data sets that your organization will wish to preserve indefinitely than data sets you're happy to delete. From scientific data to information on market behavior to social trends, the richness of our Big Data do not simply depend on the information from the last year or two or even ten. After all, if we forget the mistakes of the past then we are doomed to repeat them. Crunching today's Big Data can give us business intelligence, but only by crunching yesterday's Big Data as well can we ever expect to glean wisdom from our information.
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