Analysis and comparative exploration of elastic search, MongoDB and Hadoop big data processing

Abstract

The word “Big Data” describes innovatory tools and techniques to store, share, capture, manage and examine very large data sets with different structures. A Big Data may be unstructured, semi-structured or structured which results in incapacity of storing these data using any conventional data management methods. In order to use these data in an efficient and costless way, parallelism is used. Hadoop is open-source software and is the main platform for making Big Data structural and making it useful for different purpose. Furthermore, to solve different types of problem, different types of DBMS are being developed along with their application program interface. One of them is a MongoDB which is a very famous NoSQL database and is free from schema. It is oriented toward document whose performance for query processing is very high. Moreover, Elastic Search is a search engine which provides a way to organize data, so that it can be easily accessed. It is a tool for querying the word written. Hence, the term Elastic Search, MongoDB and Hbase are closely related. In this paper, we provide a comparative study of each one of them.