Abstract
Based on the MapReduce model and Hadoop Distributed File System (HDFS), Hadoop enables the distributed processing of large data sets across clusters with scalability and fault tolerance. Many data-intensive applications involve continuous and incremental updates of data. Understanding the scalability and cost of a Hadoop platform to handle small and independent updates of data sets sheds light on the design of scalable and cost-effective data-intensive applications. In this chapter, we introduce a motivating movie recommendation application implemented in the MapReduce model and deployed on Amazon Elastic MapReduce (EMR), a Hadoop CONTENTS 2.
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