This Big Data Analytics course (CMPS 653) provides a structured academic introduction to core concepts in distributed data processing and large-scale analytics systems. It is designed for students and learners who want to understand the theoretical and practical foundations of Big Data.
The course begins with a general introduction to Big Data and its importance in modern computing systems. It explains how massive datasets are processed and why distributed systems are required for scalability and performance.
A major part of the course focuses on MapReduce, including its design patterns and implementation strategies. Learners explore how MapReduce processes structured and unstructured data efficiently across distributed clusters.
The course also introduces Hadoop fundamentals, including HDFS architecture and data storage principles. It explains how Hadoop enables fault-tolerant and scalable processing of large datasets.
Additionally, the lectures cover advanced topics such as MapReduce design patterns, text processing, and handling unstructured data. These concepts are essential for understanding real-world Big Data applications in analytics, search systems, and large-scale data processing pipelines.
By the end of this course, learners will have a strong academic and practical understanding of Big Data systems, MapReduce programming models, and Hadoop architecture, preparing them for advanced studies or careers in data engineering and analytics.
