Prerequisites: (MET CS 521 & MET CS 544 & MET CS 555) or MET CS 577 or consent of instructor. An overview of the principles and practice of large-scale data analytics. You will examine methods for extracting meaningful insights from large, complex, and distributed datasets, learning about core technologies for storing and processing high-volume data. This course emphasizes distributed computing frameworks based on the MapReduce paradigm, including Hadoop MapReduce and Apache Spark, along with programming models, parallel data processing, and performance considerations in cluster-based environments. Through hands-on assignments and projects, you will implement data processing algorithms and deploy them on cloud platforms such as Amazon Web Services (AWS) and Google Cloud, developing the practical skills required for data engineering and large-scale analytics in real-world environments. Educational cloud accounts and credits are provided.

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