Changes

== Group Members ==

# Akhil Balachandran

This project will focus on demonstrating how a particular use case performs in Apache Hadoop versus Apache spark, and how this relates to the rising and waning adoption of Spark and Hadoop respectively. It will compare the advantages of Apache Hadoop versus Apache Spark for certain big data applications.

[https://hadoop.apache.org/ ''' Apache Hadoop'''] is an open-source framework that allows for the storage and distributed processing of large data sets across clusters of computers using simple programming models. Hadoop is an implementation of MapReduce, an application programming model developed by Google. MapReduce has three basic operations: Map, Shuffle and Reduce. Map, where each worker node applies a map function to the local data and writes the output to temporary storage. Shuffle, where worker nodes redistribute data based on output keys such that all data belonging to one key is located on the same worker node. Finally reduce, where each worker node processes each group of output in parallel.