Query Optimization of Big Data Using Hive

Authors

  • Vinay Kumar A Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Kanuru, India
  • A Madhuri Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Kanuru, India

Keywords:

Big Data, HDFS, Map Reduce, Hive, Join

Abstract

Huge amounts of data are required to build internet search engines and therefore large number of machines to process this entire data. The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of machines. The Hadoop having two modules 1. Hadoop distributed file system and 2. Map Reduce. The Hadoop distributed file system is different from the local normal file system. The HDFS can be implemented as single node cluster and multi node cluster. The large datasets are processed more efficiently by the multi node clusters. By using the hive query language on the Hadoop and increasing number of nodes the data will be processed fastest than with the fewer nodes.

References

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http://hashprompt.blogspot.in/2014/06/multi-node-hadoop-cluster-on-ubuntu-1404.html, 7 April,2015

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Published

2025-11-10

How to Cite

[1]
A. Vinay Kumar and A. Madhuri, “Query Optimization of Big Data Using Hive”, Int. J. Comp. Sci. Eng., vol. 3, no. 9, pp. 135–139, Nov. 2025.

Issue

Vol. 3 No. 9 (2015): IJCSE September Edition

Section

Research Article

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