Big Data Analytics Literature review Example | Topics and Well Written Essays - 1750 words

Big Data Analytics - Literature review Example â€Å"Big Data† as its name indicates is a collection of huge amounts of formless and meaningless data which are generated by high-quality and heavy software applications belonging to a varied group of software applications such as social networks, a wide variety of scientific computing applications, medical information systems, e-government applications, and many more. The research has shown that data that is used and processed by these different software applications share some common attributes. Some of these common characteristics can include large-scale data (which defines the distribution and size of data stores), scalability issues (it define the functionalities and features software applications processing across-the-board, huge data repositories such as big data), ensuring and maintaining advanced Extraction-Transformation-Loading (ETL) processing on low-level, unstructured and meaningless data to some extent meaningful information; designing and implementing straight forward and understandable analytics over big data stores with the purpose of attaining intelligence and extracting valuable facts and information from them. Additionally, in the past few years, analytics over big data stores has caught the attention of researchers and organizations. In addition, the research has shown various application areas where these analytics can play a significant role. In this scenario, scientific computing is believed to be one of the most important application areas for the reason than in this domain academic researchers and scientific create huge amounts of data every day in the results of their experiments and tests (for instance consider fields such as astronomy, high-energy physics, biomedicine, biology and many others). On the other hand, extracting valuable information and knowledge for different useful tasks on the basis of these huge, comprehensive data stores seems to be impracticable for common database management systems and other similar analy sis tools (Cuzzocrea, Song, & Davis, 2011; Lopez, 2012). Figure 1Big Data Process In this scenario, figure1 demonstrates the process of big data analytics. First of all data is collected from different sources. As discussed above these sources vary from social networks to different information systems and web applications. Hence, the size of this data is so huge that it is difficult to measure. In this scenario, understanding and using this data for useful tasks is almost impossible. Therefore, there is a need for a framework that could help users understand and make effective use of this data. For this purpose, there are a number of frameworks and users can select a suitable framework according to their needs and requirements. After selecting a framework, this framework is applied to data and some coding is done. After that the users can obtain results that they can use to drive decisions and perform the desired operations (Fisher, DeLine, Czerwinski, & Drucker, 2012; Lopez, 2012). Though, the term â€Å"big data† is used in different ways in different disciplines. However, in their paper (Chaudhuri, 2012) define some common characteristics of the big data idea as they have to do with analytics: Investigating unstructured data and text to determine if these sources can