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Big data is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
Big data is often characterized by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. Although big data doesn’t equate to any specific volume of data, the term is often used to describe terabytes, petabytes and even extra bytes of data captured over time.
Such voluminous data can come from myriad different sources, such as business sales records, the collected results of scientific experiments or real-time sensors used in the internet of things. Data may be raw or pre processed using separate software tools before analytics are applied. Data may also exist in a wide variety of file types, including structured data, such as SQL database stores; unstructured data, such as document files; or streaming data from sensors.
Ultimately, the value and effectiveness of big data depends on the human operators tasked with understanding the data and formulating the proper queries to direct big data projects. Some big data tools meet specialized niches and allow less technical users to make various predictions from everyday business data. Still, other tools are appearing, such as Hadoop appliances, to help businesses implement a suitable compute infrastructure to tackle big data projects, while minimizing the need for hardware and distributed compute software know-how.