Two interesting quotes about what is BigData:
When they hear the term big data, most people immediately think of large data sets; when data volumes get into the multi-terabyte and multi-petabyte range, they require different treatment. Algorithms that work fine with smaller amounts of data are often not fast or efficient enough to process larger data sets, and there’s no such thing as infinite capacity, even with storage media and management advances.
But volume is only the first dimension of the big data challenge; the other two are velocity and variety. Velocity refers to the speed requirement for collecting, processing, and using the data. Many analytical algorithms can process vast quantities of information—if you let the job run overnight. But if there’s a real-time need (such as national security or the health of a child), overnight isn’t good enough anymore.
Variety signifies the increasing array of data types—audio, video, and image data, as well as the mixing of information collected from sources as diverse as retail transactions, text messages, and genetic codes. Traditional analytics and database methods are excellent at handling data that can easily be represented in rows and columns and manipulated by commands such as select and join. But many of the artifacts that describe our world can neither be shoehorned into rows and columns, nor easily analyzed by software that depends on performing a series of selects, joins, or other relational commands.
When you add volume, variety, and velocity together, you get data that doesn’t play nice.
And how the future looks like for the companies dealing with BigData:
The big picture: Companies will probably spend less time and money defining, scrubbing, and managing the structure of data and data warehouses. Conversely, they’ll spend more time figuring out how to capture, verify, and use data quickly, so these are the skills to master.
Original title and link: BigData: The Three V’s: Volume, Variety, Velocity (NoSQL databases © myNoSQL)