Big Data is a broad term for data sets so large or complex that traditional data processing applications are not enough. It encompasses the analysis, capture, authentication of data, search, exchange, storage, transfer, visualization, consultation and privacy of information. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and rarely to define a certain size of dataset. Accuracy in Big Data can lead to more confident decision making.
The size of big data
Relational database management systems and data visualization and statistics systems often have great difficulty handling a large volume of data. This job requires some software that runs massively in parallel on tens, hundreds, or even thousands of servers. What is considered big data varies depending on the capabilities of users and their tools and also on their expansion capabilities.
The size of big data “size” has not yet been fully defined, as it can range from a few dozen terabytes to many petabytes. These huge volumes of data require a set of techniques and technologies with new forms of integration to reveal insights into datasets that are diverse, complex, and large-scale.
Big data is defined following the model of “3Vs” (volume or quantity of data), speed (speed of data input and output) and variety (variety of data types and sources).
Additional features of Big Data
The 3Vs have been extended to other complementary features of Big Data:
- Volume: Big Data does not serve as an example; just to observe and analyze what is happening.
- Speed: Big Data is often available in real time.
- Variety: Big Data is based on text, images, audio and video; plus it completes the missing pieces through data fusion.
- Machine Learning: Big Data often doesn’t ask why and simply detects patterns.
- Fingerprint: Big Data is often a no-cost byproduct of digital interaction.