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    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.

    About this paper

    Paper title Management of Large-Scale Data
    Subject Information Science
    EFTS 0.1667
    Points 20 points
    Teaching period(s) Semester 2 (Distance learning)
    Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,448.79
    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 be able to:

    • 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


    Semester 2

    Teaching method
    This paper is taught through Distance Learning
    Learning management system

    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system

    Computer Lab

    Stream Days Times Weeks
    A1 Friday 14:00-15:50 30-35, 37-42


    Stream Days Times Weeks
    A1 Wednesday 13:00-14:50 29-35, 37-42
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