Subject Descriptions - Subject Information

Calendar: 2017 Undergraduate
Faculty: Faculty of Engineering and Information Sciences
Department: School of Computing and Information Technology

Subject Information
Subject Code ISIT312
Subject Name Big Data Management
Credit Points 6
Pre-Requisites CSIT115 and 24cp @ 100 level CSIT
Co-Requisites None.
Restrictions None.
Equivalence None
General Subject Yes.

Subject Description
The subject addresses the problems of managing and processing of extremely large data sets in a single-server centralized computing systems and in multi-server clustered and distributed computing systems. The topics related to processing of large data sets in centralized environments include the techniques based on the classical data warehouse technologies such multidimensional data model, data warehouse architecture, data warehouse design both at conceptual and logical levels, and data warehouse processing with appropriate specialised query operations. The topics related to processing of large data sets in distributed environments include the techniques that can be implemented on the clusters of inexpensive computing nodes using MapReduce programming model. The subject introduces the students to the real time analytical processing of large data sets with analytical cluster-based distributed data processing systems. Discussion and hands on exercises related to these topics will equip students to meet the challenges in Big Data environments and appreciate the added challenges of dealing with unstructured data. Students will be presented with opportunities to do hands-on work with appropriate commercial tools.

Subject Learning Outcomes
On successful completion of this subject, students will be able to:
1. Formulate the principles of Big Data and summarise the issues related to design management, processing, and infrastructure requirements for implementation of Big Data systems and technologies .
2. Summarise the required functionalities of data warehousing systems and application of business intelligence software for implementation of data warehousing systems.
3. Summarize the principles of online analytical data processing (OLAP) and the applications of OLAP techniques in implementation of data warehousing systems.
4. Design and create data warehousing systems.
5. Compare centralized data warehousing systems with processing of large data sets on clustered and distributed computing systems.
6. Design and create real time data analytical applications on clustered and distributed computing systems.
7. Evaluate the other technologies available for implementation data analytical applications including those targeting large unstructured data.