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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.

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