International Journal of Electrical and Computer Engineering
Vol 10, No 4: August 2020

Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data

Santosh Jankatti (K.S.School of Engineering and Management)
Raghavendra B. K. (BGSIT (ACU) Deemed to be University)
Raghavendra S. (Christ Deemed to be University)
Meenakshi Meenakshi (Jain Deemed to be University)



Article Info

Publish Date
01 Aug 2020

Abstract

Big data is the biggest challenges as we need huge processing power system and good algorithms to make an decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.

Copyrights © 2020






Journal Info

Abbrev

IJECE

Publisher

Subject

Computer Science & IT Electrical & Electronics Engineering

Description

International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of ...