Hadoop based EMH framework: A Big Data approach

The terms like Gigabyte and megabyte have become old and outdated now because with the improvement in the field of storage technology, the storage abilities have increased and cost of storage of data has also been reduced. In present times terms like petabyte, terabyte, etc are in vogue as the e-storage capacity has increased worldwide. But at the same time, with improvement in the storage technology, the world now has ever increasing volume of data, so the need for storage is on the rise.Nowadays, huge amount of data is being generated and updated within a short span of time. Therefore, to take care of its storage, access and further advancement, analysis is required. But since the vast amount of data is getting generated in short span of time so the analysis of data as well as management of data has become a tedious, hectic and a challenging job for the data centers and managers.Various issues and challenges are there while tackling data-related issues. For example, the data is available in different types such as structured, unstructured, semi-structured, heterogeneous, or homogeneous, which makes there sorting and storage difficult. Sometimes, the older tools are not sufficient to handle all these or a combination of one or more types in real-time, or in distributed systems such as cloud systems.The creation of data and the aggregation of data is approximately in zettabytes per year. Such, volume of data is not only the problem for data management, but also affects the attributes like variety, velocity, value, and complexity. Then, there are also some technical issues as well related to the data transportation and storage.Thus, we need to think about the issues of storage and management of ever-increasing e-data and develop the novel perspective and innovative ways to address these issues.In this paper, we have analyzed these issues and challenges by studying different methodologies for big data analysis and design for Sensing and omitting medical data, e-Health record, public health record and clinical data. This paper also includes the proposed framework related to distributed data ware house based computing that has been developed to handle a larger amount of data processing by supporting smart and cost effective medical support system.