Big Data Analytics: Performance Evaluation for High Availability and Fault Tolerance using MapReduce Framework with HDFS

Big data analytics helps in analyzing structured data transaction and analytics programs that contain semi-structured and unstructured data. Internet clickstream data, mobile-phone call details, server logs are examples of big data. Relational database-oriented dataset doesn't fit in traditional data warehouse since big data set is updated frequently and large amount of data are generated in real time. Many open source solutions are available for handling this large scale data. The Hadoop Distributed File System (HDFS) is one of the solutions which helps in storing, managing, and analyzing big data. Hadoop has become a standard for distributed storage and computing in Big Data Analytic applications. It has the capability to manage distributed nodes for data storage and processing in distributed manner. Hadoop architecture is also known as Store everything now and decide how to process later. Challenges and issues of multi-node Hadoop cluster setup and configuration are discussed in this paper. The troubleshooting for high availability of nodes in different scenarios for Hadoop cluster failure are experimented with different sizes of datasets. Experimental analysis carried out in this paper helps to improve uses of Hadoop cluster effectively for research and analysis. It also provides suggestions for selecting size of Hadoop cluster as per data size and generation speed.