Critical analysis of existing Big Data analytics frame works

Recent technological advancements have led to a flood of data from various domains over the past few decades. Big Data incorporates large volume of structured, semi-structured, and unstructured data, which is beyond the processing capabilities of traditional databases. In addition to its huge volume, Big Data is commonly unstructured and requires more real-time analysis. On the other hand, the processing and analysis of Big Data plays a central role in decision making, forecasting, business analysis, product development, customer experience, and loyalty. Hence, organizations dealing with Big Data and analytics need to manage the challenges and opportunities related to datasets they have. The IT industry has responded by providing Big Data tools and technologies as well as approaches. However, many of the existing approaches and technologies experience noted limitations. In this paper, attempt has been made to examine the distinctive features of Big Data along the lines of the 3Vs (variety, volume, and velocity) using literature review and provide an understanding of the Big Data processing approaches. Furthermore, Various Big Data analytics frameworks that deal with Big Data analysis workloads were also investigated and analyzed against set of criteria. Finally, analysis and discussions of existing Big Data analytics frameworks along with a way forward approach is presented.