MapReduce

MapReduce is a Java-based, distributed execution framework within the Apache Hadoop Ecosystem. It takes away the complexity of distributed programming.

MapReduce is a programming paradigm that enables massive scalability across hundreds or thousands of servers in a Hadoop cluster. As the processing component, MapReduce is the heart of Apache Hadoop. The term "MapReduce" refers to two separate and distinct tasks that Hadoop programs perform. The first is the map job, which takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs).

The reduce job takes the output from a map as input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce job is always performed after the map job.

MapReduce programming offers several benefits to help you gain valuable insights from your big data:

• Scalability. Businesses can process petabytes of data stored in the Hadoop Distributed File System (HDFS).
• Flexibility. Hadoop enables easier access to multiple sources of data and multiple types of data.
• Speed. With parallel processing and minimal data movement, Hadoop offers fast processing of massive amounts of data.
• Simple. Developers can write code in a choice of languages, including Java, C++ and Python.