Abstract

There has been a tremendous revolution in computing technologies to handle the vast amount of data in recent years. Big data is the large-scale complex data in which real-time data is available and mushrooms the development of almost every field. In recent years, the demand and requirement of big data produced an opportunity to replace traditional data techniques due to their low efficiency and low accuracy. It shows adequate responsiveness, absence of versatility, execution, and precision for meeting the convolution of Big Data challenges. As an outcome, this created different dispersions and innovations. Big data does not mean that the data is humongous but additionally excessive in range and speed. This factor makes them tough to deal with the usage of conventional gear and techniques. Decision-makers read the extension and expansion of big data to understand and extract valuable information from rapidly varying data using big data analytics. In this chapter, we can analyze big data tools and techniques useful for big data. This chapter presents a literature survey covering various applications and technologies that play an indispensable role in offering new solutions dealing with large-scale, high-dimensional data. By summarizing different available technologies in one place from 2011 to 2019, it covers highly ranked international publications. Further, it extends in the context of computing challenges faced by significant Data Healthcare, Clinical Research, E-Commerce, Cloud Computing, Fog computing, Parallel Computing, Pervasive Computing, Reconfigurable Computing, Green Computing, Embedded Computing, Blockchain, Digital Image Processing and IoT and Computing Technology. The survey summarizes the large-scale data computing solutions that help in directing future research in a proper direction. This chapter shows that the popularity of data computing technology has steeply risen in the year 2015, and before 2011, the core research was more popular.