The Many Vs of BigData Revisited
We are living in a data-driven economy, where enterprises are increasingly collecting and processing large volumes of data in order to optimize their business processes and drive their decision making. Moreover, the millions of enterprises and the billions of internet users worldwide produce mind-boggling amounts of data. This explains why BigData is steadily one of the most trending technologies of our time. To the average reader the term BigData signifies very large data volumes, yet there is no strict threshold above which data are classified as BigData. Rather, BigData applications are defined as the ones that handle very large data volumes, which far exceed the capacity and capabilities of conventional database systems. Moreover, BigData systems are usually characterized by their ability to handle data from a great variety of heterogeneous sources, while being able to deal with streaming data that are characterized by very high ingestion rates. Volume, Variety and Velocity are three of the most prominent characteristics of BigData applications, which are commonly characterized as the 3Vs of BigData. These 3Vs were originally introduced in a paper by Gartner back in 2001 and are still the three most commonly used properties for characterizing BigData.
Over the years, there has been some inflation in the number of Vs that are used to describe and characterize BigData applications. For example, a fourth V that usually accompanies BigData description is “Veracity”, which refers to the fact that BigData applications deal with datasets that are uncertain, imprecise and difficult to trust. Likewise, several BigData experts have also introduced “Value” as a core element of the datasets of BigData applications. Value refers to the business value of the BigData application and is considered as a prerequisite for all non-trivial enterprise-scale applications. If you bet that 5Vs are enough to describe BigData, you are probably wrong as many researchers and practitioners have recently introduced more Vs in order to characterize BigData applications. The 15 most popular Vs of BigData are therefore as follows:
These 15 Vs describe some of the most commonly used properties of BigData i.e. the properties that matter the most. However, the list of Vs is not exhaustive, as other sources and articles list up to 50 different Vs. In your next BigData application it’s probably worth thinking about the Vs of your datasets and their properties that facilitate or hinder their successful processing and integration in applications with significant business value.
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