ABSTRACT
Over the past few decades, the use of computing systems has increased exponentially. Today, even complex structures like bridges and railway tracks have systems embedded within them to indicate, for example, wear and tear, and these systems can produce terabytes of data every day (Zikopoulos and Eaton 2011). Today, we often hear the phrase “Data is the new oil”. Data is a natural resource that is growing bigger. But, like any other resource, data is difficult to extract. The term “Big Data” is a misnomer, as it indicates that Big Data stands for huge data sets. However, there are many huge data sets that are not Big Data. Generally, Big Data is big where it needs to be distributed across several machines and it cannot be processed manually. Big Data is about deriving new insights from previously untouched data and integrating those insights into business operations, processes, and applications. However, everything that benefits us poses us with challenges. Most people mistake challenges for the characteristics of Big Data (volume, variety, velocity, and veracity, also known as the Four Vs). But the Four Vs are a tiny subset of the challenges posed by Big Data. As an example, let us attempt to have a better understanding of the challenges and opportunities in the Big Data domain.
