BigData: Volume, Velocity, Variability, Variety
BigData was defined as the 3 Vs: volume, variety, velocity, but Brian Hopkins (Forrester principal analyst) is adding the forth V: variability:
[BigData] it’s really about volume, velocity, variability and variety. [Velocity obviously refers to] how quickly the data comes at you and so that incorporates into the scope of big data the notion of capturing a stream of data. Then high variability and high volume are also issues. [For example, there] may be a variety of formats [as opposed to] just one relationally structured data set. You could have data from a Web log, unstructured content from the Internet, content files that are tagged with metadata and hierarchical file systems. [The concept of big data addresses] how you deal with this variety of formats and how you draw meaning from them.
When I say variability, I mean variance in meaning, in lexicon. The best example of that would be the variability problem that the [supercomputer] Watson at IBM was trying to take on. [Watson] would get an answer and would have to dissect that answer into its meaning and then use some really sophisticated parallel processing technology to try to figure out what the right question was within that three-second response time.
Original title and link: BigData: Volume, Velocity, Variability, Variety (NoSQL databases © myNoSQL)
