MapReduce: MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. Key Terms in this Chapterĭata Redundancy: It is the condition in which same piece of data is stored in two different places in the data base. The structure of the paper starts from the definition of big data, how it is recognized, analysed and transformed, then moves on to the different frameworks and platforms that are used for the analysis, and at last how the machine learning algorithms are integrated in big data analytics. The current need of the situation is to recognize the right data and extract it in the most feasible way possible without disturbing the privacy and safety of the data even if it is in large amounts. Big data is not useful as a whole, only a small part of it can be useful at a time for a specific purpose. In this way, big data creates an environment where each and every type of data is available, only the need is to properly analyze it to extract useful information from it and use it in a way for the welfare of the society.
Using big data, organizations can know the customers’ reactions to the goods and services provided by them, and they can also evaluate how the name of company is faring within the market (Grolinger, 2014 Park, 2015). This is because meaningful information can be extracted from big data (through data mining) which has proved to be helpful for making future decisions. business, transport, medical services etc (Bhardwaj, 2015). Keeping aside the fact that it is not easy to handle the big data its proper analysis can be profitable to the fields concerned with it, for e.g. which are posted online by the users of any social media platform. UGC includes all the images, posts, and videos etc. The latest example is the growth in amount of user generated content (UGC) (Backupify, 2015). Data explosion can be dated back to the period when paper and the printing press were invented, and it still occurs with the advancements introduced in the digital media (IBM, 2015). Each time some new technique for storing information was invented and a period of data explosion followed (Big Data, 2015 Storage Engine, 1951).
There have been many milestones in the history indicating this phenomenon. Sometimes it is also referred as “data tsunami” (IBM, 2014).
“Data explosion” refers to the sudden and rapid growth of data.
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