In this paper, the authors are motivated by the clear need of many organizations, companies, and researchers to deal with big data volumes efficiently. Examples include web analytics applications, scientific applications, and social networks. A popular data processing engine for big data is Hadoop MapReduce. Early versions of Hadoop MapReduce suffered from severe performance problems. Today, this is becoming history. There are many techniques that can be used with Hadoop MapReduce jobs to boost performance by orders of magnitude. In this paper, they teach such techniques. First, they will briefly familiarize the audience with Hadoop MapReduce and motivate its use for big data processing.