Abstract

The phrase “big data” has gained popularity to refer to an exciting new collection of tools and approaches for developing contemporary, data-driven applications that revolutionise the way the world computes. To the dismay of statisticians, this ubiquitous word seems to be widely utilised to include applying well-known statistical methods to big datasets for predictive purposes. Although the term “big data” has become a catchphrase, the reality is that current, distributed processing methods enable studies of datasets far more significant than those previously analysed, with astonishing results. However, distributed computing alone does not imply data science. The combination of constantly growing datasets created by the Internet and the insight that these data sets may power prediction models has resulted in a new economic paradigm known as data products. Stunning data modelling accomplishments across vast, diverse datasets. Hadoop has developed from a cluster computing abstraction to a big data operating system by offering a framework for distributed data storage and parallel processing. Spark expanded on these concepts and simplified cluster computing for data scientists. However, data scientists and analysts unfamiliar with distributed computing may believe that these technologies are designed for programmers rather than analysts. This is because paradigm changes in handling and computing data in a parallel rather than sequential approach are required. This textbook is designed to help Engineering, and Technological Undergraduates comprehend the principles, methods, and procedures involved in big data analytics with Hadoop and serve as a springboard for further exploring subject areas.

Keywords:

Big data, Hadoop

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How to cite this Book: 

APA:
Uma Devi, G. (2022). Big Data Analytics with Hadoop (1st ed., pp. 1-232). Jupiter Publications consortium,ISBN:978-93-91303-31-0, DOI:https://doi.org/10.47715/JPC.B.79.2022.9789391303310