Big Data Characteristics
Big Data Characteristics – The Four V’s of Big Data
What is the difference between regular data analysis and when are we talking about “Big” data? Although the answer to this question cannot be universally determined, there are a number of characteristics that define Big Data. In this article, we will discuss the characteristics of Big Data are commonly referred to as the four Vs.
Volume of Big Data
The volume of data refers to the size of the data sets that need to be analyzed and processed, which are now frequently larger than terabytes and petabytes. The sheer volume of the data requires distinct and different processing technologies than traditional storage and processing capabilities. In other words, this means that the data sets in Big Data are too large to process with a regular laptop or desktop processor. An example of a high-volume data set would be all credit card transactions on a day within Europe.
Figure 1: Example of card transaction
Velocity of Big Data
Velocity refers to the speed with which data is generated. High velocity data is generated with such a pace that it requires distinct (distributed) processing techniques. An example of a data that is generated with high velocity would be Twitter messages or Facebook posts.
Figure 2: Example of social media channels such as Facebook, Instagram & WhatsApp
Variety of Big Data
Variety makes Big Data really big. Big Data comes from a great variety of sources and generally is one out of three types: structured, semi structured and unstructured (as discussed in the next section). The variety in data types frequently requires distinct processing capabilities and specialist algorithms. An example of high variety data sets would be the CCTV audio and video files that are generated at various locations in a city.
Figure 3: Example of CCTV footage
Veracity of Big Data
Veracity refers to the quality of the data that is being analyzed. High veracity data has many records that are valuable to analyze and that contribute in a meaningful way to the overall results. Low veracity data, on the other hand, contains a high percentage of meaningless data. The non-valuable in these data sets is referred to as noise. An example of a high veracity data set would be data from a medical experiment or trial.
Figure 4: Example of medical experiment
Data that is high volume, high velocity and high variety must be processed with advanced tools (analytics and algorithms) to reveal meaningful information. Because of these characteristics of the data, the knowledge domain that deals with the storage, processing, and analysis of these data sets has been labeled Big Data.
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