Big Data Systems and Infrastructure

Catalog description

This course examines the architectural principles and infrastructure requirements necessary to support large-scale data processing and storage. Students explore the evolution from traditional relational database management systems (RDBMS) to modern distributed systems, focusing on the "V's" of big data (Volume, Velocity, Variety, and Veracity). The curriculum covers the design and deployment of scalable data pipelines using distributed computing frameworks, NoSQL databases, and cloud-based infrastructure. Special emphasis is placed on the trade-offs between consistency, availability, and partition tolerance (CAP Theorem) in distributed environments. Students will gain hands-on experience with industry-standard tools for batch and stream processing, data ingestion, and cluster management. By the end of the course, students will be able to architect robust, fault-tolerant infrastructures that enable high-performance analytics and support the operational needs of data-driven organizations .