|By Adrian Bridgwater||
|May 28, 2013 08:00 AM EDT||
The IT industry is nothing if not a breeding ground for an infinite variety of acronyms and neologisms. Alongside cloud computing today sits the term Big Data, which of course we understand to mean "that amount" of data which a traditional database would find hard to compute and process as a normal matter of job processing.
But what is a neologism if you can't turn it into a neo-neologism? Big Data in its own right is a term that we are just about getting used to, but the sooner we move towards an appreciation of ‘fast Big Data' the better.
Technology analysts have been fond of the standard 'four Vs' definition used to describe the shape of Big Data, i.e., volume, velocity, variety and variability - but it is the ‘velocity' factor that sits somewhat incongruently among its V-shaped bedfellows, i.e., it is the only factor that describes speed or motion. Without a velocity layer, Big Data lies in a state of inertia.
In the new world of data 2.0 we find that the velocity factor is extremely important. More of our computing channels are described as real-time or near real-time (both definitions are important) as users demand applications that rely upon ubiquitous connections to the Internet, other users, other data events and other application services.
Suddenly speed has become an integral part of the Big Data ethos, yet it is mentioned with comparative scarcity. Press and analyst (and vendor) comment pieces talk up the zany incomprehensible world of zettabytes, petabytes and yottabytes. These are low hanging fruit and easy to comment on. Forget terabytes, they are so 2009.
Speed Is the Unloved Second Cousin of Big Data
If speed is the unloved second cousin of Big Data, it shouldn't be. Major enterprise players (the vendors, not the customers in the first instance) are spending their hard-earned acquisition and development dollars on the technology positioned as the antidote to our Big Data woes, namely "analytics" - and analytics without real-time analytics is like a car at full throttle without a steering wheel, i.e., we need to be able to react to data in the real world and navigate through it without crashing.
Of course the real fact of the matter here is that Big Data should be considered for its size, girth and overall hugeness as much as for its speed of movement. To contemplate an analysis of one without the other is fallacious and foolhardy. These two factors form two mutually interdependent sides of the contemporary data balancing equation that props up the Big Data economic model.
Software requirements in terms of compute capacity and depth of storage (okay that's hardware, we know) both increase proportionally as the economic values for data and time approach zero. As fast real time Big Data comes of age, we need more back office technology to support it.
None of this happens without layers of management technology and this is where much of the industry discussion is focused today with regard to Big Data. The trouble is, people aren't calling it fast Big Data yet. It will happen, but it needs to happen in real time and that means today.
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