Hadoop MapReduce

Learning Goal: I’m working on a databases case study and need support to help me learn.

Many organizations, businesses, and researchers are working to deal with large amounts of data

in an effective manner. Web applications by analytics , social networks and scientific

applications are some examples. Hadoop MapReduce is a prominent large data processing

engine. Early versions of Hadoop MapReduce had serious performance issues. This is quickly

becoming history. There are several approaches that may be utilized with Hadoop MapReduce

tasks to significantly improve performance. Such strategies are covered in this lesson. First, we

will introduce Hadoop MapReduce to the audience and explain why it is useful for huge data

processing. Then, we’ll look at various data management approaches, ranging from job efficiency

to physical data organizing techniques like data layouts and indexes. We will accommodate the

similarities and differences between of two throughout this. Parallel DBMS and Hadoop

MapReduce. In addition, we will highlight unsolved research topics and open issues.