{"id":12745,"date":"2012-07-09T16:42:01","date_gmt":"2012-07-09T16:42:01","guid":{"rendered":"https:\/\/www.techopedia.com\/definition\/big-data-analytics\/"},"modified":"2024-05-31T12:03:07","modified_gmt":"2024-05-31T12:03:07","slug":"big-data-analytics","status":"publish","type":"definition","link":"https:\/\/www.techopedia.com\/definition\/28659\/big-data-analytics","title":{"rendered":"Big Data Analytics"},"content":{"rendered":"

What is Big Data Analytics?<\/span><\/h2>\n

Big data analytics is a complex process that involves collecting, cleaning<\/a>, transforming<\/a>, analyzing, and interpreting large amounts of structured<\/a>, unstructured<\/a>, and semi-structured<\/a> data.<\/p>\n

The goal of this type of analytics is to discover meaningful patterns that can be used to make data-driven<\/a> decisions.<\/p>\n

Big data analytics handles much larger and more diverse data sets<\/a> than traditional data analytics<\/a>, so it requires more compute<\/a> resources and different types of analytics platforms<\/a> and data visualization<\/a> tools.<\/p>\n

To make large volumes of big data<\/a> easier to manage, raw data<\/a> is often cleaned and preprocessed<\/a> in the cloud<\/a>. To gain actionable insights in a reasonable amount of time, analysts use machine learning<\/a> (ML) algorithms that can efficiently parse<\/a> the data, identify significant patterns, and predict future trends based on historical information.<\/p>\n

\"What<\/p>\n

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5 Key Takeaways<\/span><\/h2>\n