Big data is the IT industry’s hottest buzz word. Everyone from developers to decision makers, from a small startups to big names are dealing in it.
There are so many resources available online, which give complex theories, but in simple terms what is big data and what is its use because of which industry is crazy about it?
As per Gartner‘s 2012 updated definition “Big data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”
In simple terms we can say Big data is data which is too large, moves too fast, and doesn’t fit into structure of relational database. The size of big data goes into terabytes or petabytes.
Big data has the characteristic of three V’s: volume, velocity, and variety. Volume is the size of data TB or PB, velocity is the speed at which data is getting generated such as continuos stream,variety is the type of data such as video files, twitter feeds, page clicks, log files.
Companies are using big data to extract information from large amount of data quickly. If processing the huge data and extracting the meaningful information from it takes time, then that information might lose its value.
For example consider the product suggestion on any e-commerce website. The site which displays the right product suggestions to the customer at right time, gains most from it. The product suggestion won’t be useful later, after say a minute, because at that time user might have left or have lost interest..
The other uses of big data are processing the video feed of the user in a departmental store and extracting the information such as user’s face expression, mood when he picks a particular product, identifying the fraud transactions from millions of transactions happening every second, scanning twitter feed and acting on the information relevant to me.
The way big data can be used is huge, it’s intended to make online world more secure (detecting frauds) and smart (real-time suggestions and analysis).
Recommended learning: http://cdn.oreillystatic.com/oreilly/radarreport/0636920025559/Planning_for_Big_Data.pdf