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INFO408 Management of Large-Scale Data

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. Over the last decade, improvements in storage, computer and networking infrastructure have made it possible to store, process and transport an ever-increasing amount of data, while the rise of data-driven companies like Google and Facebook has driven 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
Paper code INFO408
Subject Information Science
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,282.09
International Tuition Fees (NZD) $5,357.07

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Limited to
MA, MBus, MCom, MSc, MAppSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc , PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
Eligibility
Suitable for graduates and professionals who have some prior experience with databases and Structured Query Language (SQL).
Contact
nigel.stanger@otago.ac.nz
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
  • Data distribution
  • Ethics of big data
  • Teaching Arrangements
    One 2-hour class per week
    One 1-hour lab each week
    Textbooks
    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 deal with architectural and implementation performance bottlenecks in big data environments

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    Timetable

    First Semester

    Location
    Dunedin
    Teaching method
    This paper is taught On Campus
    Learning management system
    Blackboard

    Computer Lab

    Stream Days Times Weeks
    Attend
    L1 Thursday 16:00-16:50 9-13, 15, 18-22

    Lecture

    Stream Days Times Weeks
    Attend
    L1 Wednesday 14:00-15:50 9-13, 15-16, 18-22