A study of advanced topics relating to the management of extremely large-scale data sets (’big data’).
The modern world is awash in a sea of data. Ongoing improvements in storage, computer, and networking infrastructure make it possible to store, process, and transport an ever-increasing amount of data, while data-driven companies like Google and Facebook drive rapid developments in the software used to manage these data. INFO 408 introduces future data scientists to the concepts and issues associated with the management and use of such 'big data' databases.
|Paper title||Management of Large-Scale Data|
|Teaching period(s)||Semester 2
Semester 2 (On campus)
|Domestic Tuition Fees (NZD)||$1,409.28|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- Limited to
- MA, MBus, MCom, MSc, MAppSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc , PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
- Suitable for graduates and professionals who have some prior experience with databases and Structured Query Language (SQL).
- Teaching staff
Co-ordinator: Dr Nigel Stanger
Support: Professor Stephen Cranefield
- Paper Structure
The major topic areas covered are:
- Big data architectures
- Scalability and performance
- Distributed data management and processing
- Ethics of big data
- Teaching Arrangements
The Distance Learning offering of this paper is taught remotely.
One x 2-hour class each week
One x 2-hour lab each week
Textbooks are not required for this paper.
- Course outline
- View the most recent Course Outline
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical
thinking, Ethics, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete this paper will
- Discuss ethical implications and issues raised by big data, and how these may be addressed
- Compare and contrast different software and infrastructural architectures that can be applied to big data management, and choose an appropriate architecture for a given problem
- Compare and contrast different implementation approaches for big data systems
- Design, build, and use a large-scale database
- Identify and deal with architectural and implementation performance bottlenecks in big data environments