Big data

Each time day to day work life of an employee or an organization depend on raw data from different sources. Data was the base for create information, using some instructions, formula or algorithm. We convert that set of data in to set of information to get some scheduled output according to the job or task. That was the simplest way of information generation through data which need little or average computational capacity. This information gives much more advance set of data than the raw data, which helps decisions making.

 

But in modern world most of the data and information linked each other every time through internet, we called it as networked data. Each data has own set of sub data or the reference links, shares, likes, comments, attachments, notes with it. Therefore those set of data had its own environment, which cannot separate data from its environment. Through this collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. These kind of data sets we simply called as “big data”, because of its behavior and capacity. 

 

Big data that may help to identify lots of trends, behaviors, patterns and so on. But the challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data. Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".