New Search

Advanced search
Export article
Open Access

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

Santosh Jankatti, Raghavendra B. K., Raghavendra S., Meenakshi Meenakshi
International Journal of Electrical and Computer Engineering (IJECE) , Volume 10, pp 3811-3818; doi:10.11591/ijece.v10i4.pp3811-3818

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.
Keywords: Big Data / machine learning / spark / Apache / say / make an decision / pig hive

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

Share this article

Back to Top Top