While<\/em> the concept of big data isn\u2019t new, most businesses have recently realized that if they can capture all the data which streams into their operations, analytics can be applied and significant value can be derived from that. Now, the massive amounts of data only become useful when big data analytics<\/strong> is performed to identify patterns and insights that would be left undiscovered otherwise. As a result, businesses are increasingly looking for professionals who\u2019re familiar with various big data analytics tools <\/strong>to get help in attaining their goals. Here\u2019s an overview of some popular big data analytics tools.<\/strong><\/p>\n

1- Hadoop<\/em><\/strong><\/h3>\n

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One<\/em> of the most popular big data analytics tools<\/strong>, Hadoop is an open-source framework and provides massive storage for all types of data. With its exceptional processing power and ability to deal with numerous tasks, Hadoop keeps professionals from worrying about hardware failure.<\/p>\n

2- OpenRefine<\/em><\/strong><\/h3>\n

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This<\/em> big data analytics<\/strong> tool lets professionals clean up data for analysis. It comes with cells under columns which is similar to relational database tables. With this tool, you\u2019d be able to perform things like cleaning messy data, the transformation of data, parsing data from websites etc.<\/p>\n

3- RapidMiner<\/em><\/strong><\/h3>\n

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RapidMiner<\/em> is one of the big data analytics tools <\/strong>that offer machine learning procedures together with data mining techniques like data visualization, processing, predictive analytics etc. Apart from business and commercial applications, this big data analytics<\/strong> tool is used for application development.<\/p>\n

4- Apache Spark<\/em><\/strong><\/h3>\n

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It\u2019s<\/em> an open-source and powerful big data analytics<\/strong> tool that comes with a huge number of high-level operators which make it easy to develop parallel apps. It not only offers lightning-fast processing but also comes with lots of abilities including helping in running an application in Hadoop cluster, offering built-in APIs in Python, Scalar, or Java, being able to integrate with Hadoop etc.<\/p>\n

5- MongoDB<\/em><\/strong><\/h3>\n

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This<\/em> big data analytics<\/strong> tool is a contemporary alternative to databases. Its best application can be found when it comes to working with databases that change or vary frequently or the ones which are unstructured or semi-structured. Some of its best uses include product catalogs, content management systems etc.<\/p>\n

6- Lumify<\/em><\/strong><\/h3>\n

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You<\/em> can consider this big data analytics<\/strong> tool as a big data analysis, fusion, and visualization platform. It helps professionals to explore relationships and explore connections in their data through a suite of analytic options. It\u2019s built on scalable big data technologies and comes with interface elements for images, videos, and textual content.<\/p>\n

7- Tableau<\/em><\/strong><\/h3>\n

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This<\/em> is one of the leaders in big data analytics tools<\/strong> and a viable option for non-data scientists engaged in different organizations. A big benefit of using this big data analytics<\/strong> tool is that professionals can reuse existing skills when it comes to big data. Tableau uses a standardized SQL to query, as well as, interface with big data systems and thus, makes it possible for companies to use an existing database to identify the insights they\u2019re looking for, from a massive dataset. It\u2019s also equipped with the VizQL data visualization technology that allows for data visualization without organizing the data first.<\/p>\n