Big data is a term used to describe extremely large data sets that traditional database applications cannot deal with. Big data sets are often defined in terms of:
Volume - refers to the amount of data
Variety - refers to the number of types of data
Velocity - refers to the speed of data processing as the incoming rate can be extremely high
Volume
Social media sites, for example, store photographs. Let's consider the volume of this data. Take a social media site with 1.74 billion users, who between them upload 300 million photos per day. It is currently storing roughly 250 billion images. With 300 million uploads per day, 250 billion images will seem an almost insignificant volume of data to store compared to that required in six months time.
Variety
As well as the traditional structured data types (text, number, currency, Boolean), social media sites have to store a massive variety of unstructured data types (photographs, videos, likes, comments, messages, audio recordings etc.).
Velocity
Velocity is the measure of how fast data is coming in. Modern social media sites must handle huge volumes of data every day. They must ingest it all, process it, file it, and be able to retrieve it. Data now arrives into servers continually and in real time, and results are only useful if the delay in processing this data is very short.
Big data analytics
To discover hidden patterns, trends and customer preferences, organisations must analyse the vast amounts of data that they hold.
Big Data Analytics Applications provide a means of analysing these huge data sets and drawing conclusions to help organisations make informed business decisions, such as targeted marketing, better customer service or identifying new business opportunities.
For example, social media sites can very accurately predict your intelligence, emotional stability, religion, relationship status, age, gender, race, sexual orientation and political views (among many other things) from the data you are providing.