MapReduce Algorithms for Big Data Analysis

There is a growing trend of applications that should handle big data. However, analyzing big data is very challenging today. For such applications, the MapReduce framework has recently attracted a lot of attention. MapReduce is a programming model that allows easy development of scalable parallel applications to process big data on large clusters of commodity machines. Google's MapReduce or its open-source equivalent Hadoop is a powerful tool for building such applications. In this tutorial, I will first introduce the MapReduce framework based on Hadoop system available to everyone to run distributed computing algorithms using MapReduce. I will next discuss how to design efficient MapReduce algorithms and present the state-of-the-art in MapReduce algorithms for big data analysis. Since Spark is recently developed to overcome the shortcomings of MapReduce which is not optimized for of iterative algorithms and interactive data analysis, I will also present an outline of Spark as well as the differences between MapReduce and Spark. The intended audience of this tutorial is professionals who plan to develop efficient MapReduce algorithms and researchers who should be aware of the state-of-the-art in MapReduce algorithms available today for big data analysis.