Course contents: The course focuses on distributed system architectures, cluster computing systems, grids and cloud technologies, distributed file systems (e.g., Google file system, Hadoop, Facebook Cassandra), search of distributed data (Chord), models of parallel/distributed computations for relational data (Map/Reduce) and graphs (Pregel), NoSQL databases (e.g., Elasticsearch, MongoDB, Neo4j), big data visualization (e.g., Kibana), big data streams, and big data mining. Additionally, the course discusses issues concerning the use of big data in the context of different everyday applications (e.g., social networks, e-health, e-government, etc.), as well as the related ethical/private issues raised.

Assessment: The course grade will be based on programming projects (possibly involving a personal examination) and/or exercises (in-class or homework) and/or oral presentation that will jointly account for 50% of the final grade, and a 3-hour written final examination that will account for the remaining 50% of the course grade. These percentages may vary (+/-10%) each year. In order for a student to successfully complete the course, s/he must score at least 50% in the written exams, and the student’s weighted average should be 50% or higher.