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||Second Semester|
|Domestic Tuition Fees (NZD)||$1,333.93|
|International Tuition Fees (NZD)||$5,793.66|
- 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).
- More information link
- View more information about INFO 408
- Teaching staff
- Paper Structure
The major topic areas covered are:
- Big data architectures (Relational and non-Relational)
- Scalability and performance
- Distributed data management and processing
- Ethics of big data
- Teaching Arrangements
One x 2-hour class each week
One x 2-hour lab each week
- Text books 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
- 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 and build a large, scalable database, and use it to answer complex queries.
- Identify and deal with architectural and implementation performance bottlenecks in big data environments.