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With the development of massive search engines (such as Google and Yahoo!), genomic analysis (in DNA sequencing, RNA sequencing, and biomarker analysis), and social networks (such as Facebook and Twitter), the volumes of data being generated and processed have crossed the petabytes threshold. To satisfy these massive computational requirements, we need efficient, scalable, and parallel algorithms. One framework to tackle these problems is the MapReduce paradigm. MapReduce is a software framework for processing large (giga-, tera-, or petabytes) data sets in a parallel and distributed fashion, and an execution framework for large- scale data processing on clusters of commodity servers. There are many ways to implement MapReduce, but in this book our primary focus will be Apache Spark and MapReduce/Hadoop. You will learn how to implement MapReduce in Spark and Hadoop through simple and concrete examples.