Question

Having learned the hard core MapReduce Programming techniques, what is your opinion about MapReduce programming for solving big data business problems.   Here are the options for your rating- 1)YUCK, 2)MEH, 3)GOOD, 4)AWESOME. Please choose one of the above rating with an explanation. Please give me your honest opinion about Mapreduce programming only! (not other topics like HDFS architecture, or HDFS programming of Hadoop or YARN in general).

Answer 1

The map-reduce programming for solving big data business problems is awesome in my opinion because the world’s largest companies like Yahoo, Facebook, Amazon, and IBM use the MapReduce model as a too for the cloud computing applications through implementing Hadoop, an open-source code of MapReduce, produced by Apache software foundation. Some implementations with a different approach are developed later, such as the Dryad, Phoenix, Mars, Twister, and GridGain. The contribution of this section is to provide an overview of the existing implementation of MapReduce applications.

MapReduce is designed to be fault-tolerant because failures are a common phenomenon in large scale distributed computing. Google’s MapReduce architecture seems to be a good choice for several reasons :

•Information processing tasks benefit from parallel and distributed architecture with simply the programming of Map and Reduce methods.

•MapReduce architecture has the ability to process terabytes of data on PC clusters with handling failures.

•Most of the data recovery and excavating information can be taken into MapReduce architecture, similar to the pattern-based annotation algorithms. Distributed Grep is one of the basic examples for MapReduce using the One pattern approach with regular expressions as well.