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 the rise of 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||First Semester|
|Domestic Tuition Fees (NZD)||$1,307.76|
|International Tuition Fees (NZD)||$5,517.77|
- 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
- Coordinator: Dr Nigel Stanger
Support: Professor Stephen Cranefield
- Paper Structure
The major topic areas covered are:
- Big data architectures (Relational and non-Relational)
- Querying large-scale databases
- Scalability and performance
- Distributed data management and processing
- Ethics of big data
- Teaching Arrangements
- One 2-hour class per week
One 1-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 the ethical implications and issues associated with 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 (e.g. Relational vs NoSQL)
- Design and build a large, scalable database or data warehouse and use it to answer complex analytical queries
- Identify and discuss architectural and implementation trade-offs and performance bottlenecks in big data environments