The methods used for data processing in Big Data and Data Science are very different. The goal of data science is to apply analytical, machine learning, and statistical techniques to extract insights from both organized and unstructured data. Regression analysis, clustering, and decision trees are often used techniques to extract predictive insights and patterns. Big Data, on the other hand, focuses on processing and analyzing incredibly massive datasets that are challenging to manage with conventional data processing technologies. It manages and processes data in a distributed environment utilizing technologies such as Hadoop, Spark, and NoSQL databases, emphasizing volume, velocity, and variety to handle scalability and real-time analysis.
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