Mahout started out primarily as a Java MapReduce package to run distributed and scalable machine learning algorithms on top of Hadoop. As the Mahout Project matures, it has taken a decision to move out of MapReduce and embrace Apache Spark and other distributed processing frameworks, such as H20, with a focus on write once and run on multiple platforms. In this chapter, we are going to discuss:

MapReduce and HDFS were two paradigms largely responsible for a quantum shift in data processing capability. With increased capabilities, we learned to imagine larger problems that kick started a whole new industry of Big Data Analytics. The last decade has been amazing for solving data-related problems. However, in recent times, a lot of effort has been put into developing processing paradigms beyond MapReduce. These efforts are either aimed at replacing MapReduce or augmenting the processing framework...