MapReduce: Simplified Data Analysis of Big Data

With the development of computer technology, there is a tremendous increase in the growth of data. Scientists are overwhelmed with this increasing amount of data processing needs which is getting arisen from every science field. A big problem has been encountered in various fields for making the full use of these large scale data which support decision making. Data mining is the technique that can discovers new patterns from large data sets. For many years it has been studied in all kinds of application area and thus many data mining methods have been developed and applied to practice. But there was a tremendous increase in the amount of data, their computation and analyses in recent years. In such situation most classical data mining methods became out of reach in practice to handle such big data. Efficient parallel/concurrent algorithms and implementation techniques are the key to meeting the scalability and performance requirements entailed in such large scale data mining analyses. Number of parallel algorithms has been implemented by making the use of different parallelization techniques which can be listed as: threads, MPI, MapReduce, and mash-up or workflow technologies that yields different performance and usability characteristics. MPI model is found to be efficient in computing the rigorous problems, especially in simulation. But it is not easy to be used in real. MapReduce is developed from the data analysis model of the information retrieval field and is a cloud technology. Till now, several MapReduce architectures has been developed for handling the big data. The most famous is the Google. The other one having such features is Hadoop which is the most popular open source MapReduce software adopted by many huge IT companies, such as Yahoo, Facebook, eBay and so on. In this paper, we focus specifically on Hadoop and its implementation of MapReduce for analytical processing.