A Comparative Study on Performance of Hadoop File System with MapR File system to Process Big Data Records
Big Data is a buzz word heard everywhere and many organizations are generating huge amounts of data. The data is growing at faster pace. Variety of data stored is posing a new
challenge for the organizations. Organizations need a new set of tools and techniques which can efficiently process, analyze and visualize the data for better decision making. Distributed systems developed by the developers can run on various nodes to an extent can solve the problem of data processing. Development of
cloud applications is an advantage to the organizations to process the huge data on the cloud. Hadoop and its ecosystem will help to efficiently process the data by using commodity hardware. MapReduce is a framework for writing programmes for Hadoop system. Hadoop Distributed File System (HDFS) is the storage system for storing large data on commodity hardware. Hadoop file system still faces few challenges. Recently MapR has developed the MapR file system to distribute the large data sets and it overcomes the challenges faced by the Hadoop file system. In this paper we first study about the Hadoop file system, its limitations and then make a comparative study of MapR file system. Also we analyze how the MapR system is more efficient in distributing the data than Hadoop file system. We also analyze how MapR system overcomes the limitations of Hadoop File System.
Keywords: Big Data, Cloud, Hadoop, HDFS, MapR
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ABOUT THE AUTHORS
T. Suryakanthi
Dr. T. Suryakanthi earned her master’s degree in computer applications in 2002 from Andhra University, Visakhapatnam, India and doctoral degree in 2014 from Lingaya’s University, Faridabad, India. She has worked for around 2 years in software industry and has been teaching for 2 and half years. She was Assistant Professor of Computer Applications at Lingaya’s University and is currently associated with Botho University, Gaborone, Botswana. She is a member of IEEE, ACM, IAEng. She has 11 research papers to her credit in international conferences and journals. Her current research interests include Artificial Intelligence, Natural Language Processing, Machine Translation, Big data analytics and Theory of automata.
V. S. J. Pallapolu
Mrs. V. S. J. Pallapolu earned her master’s degree in computer applications in 2008 from Acharya Nagarjuna University, Guntur, India. She is in teaching from the past 4 years. She is currently associated with University of Botswana, Gaborone, Botswana. She has not only taught in fulltime courses and also contributed to Distance programs in the University of Botswana .She has published a paper in International Journal and presented two (2) papers in international conferences. Her current research interests include big data analytics, business intelligence, and Information management.
T. Suryakanthi
Dr. T. Suryakanthi earned her master’s degree in computer applications in 2002 from Andhra University, Visakhapatnam, India and doctoral degree in 2014 from Lingaya’s University, Faridabad, India. She has worked for around 2 years in software industry and has been teaching for 2 and half years. She was Assistant Professor of Computer Applications at Lingaya’s University and is currently associated with Botho University, Gaborone, Botswana. She is a member of IEEE, ACM, IAEng. She has 11 research papers to her credit in international conferences and journals. Her current research interests include Artificial Intelligence, Natural Language Processing, Machine Translation, Big data analytics and Theory of automata.
V. S. J. Pallapolu
Mrs. V. S. J. Pallapolu earned her master’s degree in computer applications in 2008 from Acharya Nagarjuna University, Guntur, India. She is in teaching from the past 4 years. She is currently associated with University of Botswana, Gaborone, Botswana. She has not only taught in fulltime courses and also contributed to Distance programs in the University of Botswana .She has published a paper in International Journal and presented two (2) papers in international conferences. Her current research interests include big data analytics, business intelligence, and Information management.
