In consideration to fact that the popularity of word ‘Hadoop’, ‘Big Data’, and ‘Analytics’ is relevantly high and therefore, for those who are interested in working in the field of Analytics, should understand that the three words have completely different meaning. Therefore, it is no justice to use them interchangeably.
What Differentiates Hadoop from Big Data?
Hadoop is a software framework used for storing and processing of Big Data. The open source tool is built on Java platform and focuses on performance improvisation on the formation of clusters of commodity hardware. While considering the Big Data and Hadoop going parallel to each other, it is important to understand that both have distinct job descriptions. Therefore, Big Data just acts like a fuel that Hadoop work on to convert into a form that is easy for analysis. In addition, a person who is able to write down the code and the related frameworks cannot necessarily understand the related patterns and work on actionable insights. This work area should be assigned to a data scientist. The two terms have completely different job descriptions.
What Makes “Big Data” Efficient?
Big is a term that is described using the four Vs such as Velocity of data, Volume of data, Variety of data and Veracity of data. The term is can be explained through the following example, HR data fetches low volume, and velocity of data as the data requires low computing power. Thus, even the low power for calculating and processing HR data seems like big data for the practitioners. It implies that big data is efficient and has outlived its usefulness.
Although, the terms have complete different functionalities yet ‘Big data’ and ‘Hadoop’ creates opportunities like never before. It is anticipated that “smart data” should replace “big data” for most of the analytical applications!