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Spark's unique use case is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations to allow data scientists to tackle the complexities that come with raw unstructured datasets. Next, we will help you become comfortable and confident working with Spark for data science by exploring Spark's data science libraries on a dataset of tweets. He has worked on various technologies including major databases, application development platforms, web technologies, and big data technologies. His typical day includes building efficient processing with advanced machine learning algorithms, easy SQL, streaming and graph analytics.