ARMONK, N.Y., Nov. 20, 2014 /PRNewswire/ -- IBM (NYSE: IBM) today announced that it is bringing a greater level of control, security and flexibility to cloud-based application development and delivery with a single-tenant version of Bluemix, IBM's platform-as-a-service. The new platform enables developers to build ap...
|By Jason Bloomberg||
|February 28, 2013 08:00 AM EST||
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
So much of our digital lives have taken place over the last twenty years or so that we forget that digital computing dates back to the 1940s-and furthermore, we forget that this sixty-odd year lifetime of the Information Age is really only the first act of perhaps centuries of computing before humankind either evolves past zeroes and ones altogether or kills itself off in the process. Our technologies for archiving information, however, are woefully shortsighted, for several reasons:
- Hardware obsolescence (three to five years) - Using a hard drive or tape drive for archiving? It won't be long till the hardware is obsolete. You may get more life out of the gear you own, but one it wears out, you'll be stuck. Anyone who archived to laser disk in the 1980s has been down this road.
- File format obsolescence (five to ten years) - True, today's Office products can probably read that file originally saved in the Microsoft Excel version 1 file format back in the day, but what about those VisiCalc or Lotus 123 files? Tools that will convert such files to their modern equivalents will eventually grow increasingly scarce, and you always risk the possibility that they won't handle the conversion properly, leading to data corruption. If your data are encrypted, then your encryption format falls into the file format obsolescence bucket as well. And what about the programs themselves? From simple spreadsheet formulas to complex legacy spaghetti code, how do you archive algorithms in an obsolescence-proof format?
- Media obsolescence (ten to fifteen years) - CD-ROMs and digital backup tapes have an expected lifetime. Keeping them cool and dry can extend their life, but actually using them will shorten it. Do you really want to rely upon a fifteen-year-old backup tape for critical information?
- Computing paradigm obsolescence (fifty years perhaps; it's anybody's guess) - will quantum computing or biological processors or some other futuristic gear drive binary digital technologies into the Stone Age? Only time will tell. But if you are forward thinking enough to archive information for the 22nd century, there's no telling what you'll need to do to maintain the viability of your archives in a post-binary world.
Cloud to the Rescue?
On the surface, letting your Cloud Service Provider (CSP) archive your data solves many of these issues. Not only are the new archiving services like Amazon Glacier impressively cost-effective, but we can feel reasonably comfortable counting on today's CSPs to migrate our data from one hardware/media platform to the next over time as technology advances. So, can Cloud solve all your archiving issues?
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
You might think that the challenges inherent in archiving Big Data are simply a matter of degree: bigger storage for bigger data sets, right? But thinking of Big Data as little more than extra-large data sets misses the big picture of the importance of Big Data.
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
The obvious question that comes to mind is whether we need to save all those old data sets anyway. After all, who cares about, say, purchasing data from 1982? And of course, you may have a business reason for deleting old information. Since information you preserve may be subject to lawsuits or other unpleasantness, you may wish to delete data once it's legal to do so.
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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