Open AccessReview of Leading Data Analytics Tools
Author(s) -
Sridevi Bonthu,
K. Hima Bindu
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.31.18190
Subject(s) - python (programming language) , analytics , data science , computer science , variety (cybernetics) , software analytics , cultural analytics , data analysis , spark (programming language) , software , big data , world wide web , semantic analytics , data mining , software development , the internet , artificial intelligence , software construction , web modeling , programming language , operating system
Data Analytics has become increasingly popular in uncovering hidden patterns, correlations, and other insights by examining large amounts of data. This led to the emergence of a variety of software tools to analyze data. Before adopting the tool, organizations need to know how they will fit into their larger business goals. Due to ever changing requirements from people practicing Data Analytics, many new tools are entering into the market and few tools are losing importance. A review of current popular tools is provided in this paper to help the analytics practitioners to choose the appropriate tool for the requirement at hand. This paper provides a review of seven popular tools viz., R, Python, RapidMiner, Hadoop, Spark, Tableau, and KNIME.