Apache hadoop

In the Hadoop framework, MapReduce is the programming model. MapReduce utilizes the map and reduce strategy for the analysis of data.

In today’s fast-paced world, there is a huge number of data available, and processing this extensive data is one of the critical tasks to do so. However, the MapReduce programming model can be the solution for processing extensive data while maintaining both speed and efficiency.

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Understanding this programming model, its components, and execution workflow in the Hadoop framework will be helpful to gain valuable insights.

What is MapReduce?

MapReduce is a parallel, distributed programming model in the Hadoop framework that can be used to access the extensive data stored in the Hadoop Distributed File System (HDFS).

The Hadoop is capable of running the MapReduce program written in various languages such as Java, Ruby, and Python. One of the beneficial factors that MapReduce aids is that MapReduce programs are inherently parallel, making the very large scale easier for data analysis.

When the MapR

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