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Hadoop Technology for Bigdata Analytics

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Document pages: 5 pages

Abstract: Big data is the word that entire world talks today. Day by day the volume of data is increasing from Kilo bytes to Zettabytes, the velocity that the data may be continuous or frequent streams which is common in the real time and variety of data available from different sources in different formats (structured, unstructured, and semi-structured). All these types of data are to be merged, stored, processed and analysed for future outcome. Big data analytics is popular because of its cost reduction, faster and better decision making. Due to its specific features it is used in number of applications like Healthcare, Education, Manufacturing, Banking, Insurance, Transportation, Media and Entertainment etc. The data in healthcare is increasing rapidly and is expected to increase significantly in recent years. In today’s digital world, it is mandatory that data should be digitized. To improve the quality of healthcare by minimizing the costs, it’s necessary that different kinds of health data like electronic health records, genomic, behavioural and public health should be processed and analysed effectively to answer new challenges. With this reason Healthcare domain is considered for Bigdata analytics. This paper describes predictive, prescriptive, descriptive and diagnostic types of analytics for processing health care records. To perform all these operations Hadoop is the best choice. Hadoop is the combination of Hadoop Distributed File System (HDFS) and MapReduce. Hadoop is familiar due to its storage capacity, higher processing speed, low cost, simple and efficient model that works in distributed environment by using clusters. Hence, understanding the technical details of Hadoop becomes essential. This fact inspired to explore Hadoop and its components in-depth. MapReduce result helps to predict epidemics, cure diseases, improve quality of life and also prevents from deaths.

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