Courses & Units

Big Data and Cloud Computing KIT318

Hobart, Launceston


In recent years, due to advancement of internet technologies and instrumentation of every part of our life, we have noticed a huge surge in data available to us. This revolution is termed as Big Data. This Big Data cannot be processed or managed by any traditional methods of processing. This has led to development of several high performance and distributed computing platforms and programming frameworks. The design of such platforms relies on distributed computing concepts which are implemented in the form of systems such as Clusters and Clouds, and Big Data frameworks such as MapReduce and Stream Computing. These systems play an important role in todays' research, academia or industries by providing the processing of data generated from a variety of networked resources, e.g. large data stores and information repositories, expensive instruments, social media, sensors networks, and multimedia services for a wide range of applications.

The aim of this unit is to provide students with the foundation knowledge and understanding of Big Data and distributed computing systems and applications especially in context of Cloud. In other words, this unit will equip students with essential knowledge that is needed for building next-generation applications that are scalable and efficient and can process Big Data.

Key topics that will be covered: parallel systems: parallel paradigms, parallel and distributed algorithms, and building parallel applications using MPI; Cluster computing: cluster fundamentals and architecture; Big Data: MapReduce platforms, Stream Computing platforms and Algorithms. Cloud computing: Cloud technologies, virtualization, programming model, resource management and scheduling, application building for managing and analyzing data. The unit will also explain how the business models of enterprises are changing with these forms of computing that provide large storage and computation space without purchasing expensive computer systems.


Unit name Big Data and Cloud Computing
Unit code KIT318
Credit points 12.5
College/School College of Sciences and Engineering
School of Information and Communication Technology
Discipline Information & Communication Technology
Coordinator Doctor Saurabh Garg
Available as an elective?
Delivered By Delivered wholly by the provider
Level Advanced


Location Study period Attendance options Available to
Hobart Semester 1 On-Campus International Domestic
Launceston Semester 1 On-Campus International Domestic


International students
Domestic students

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Key Dates

Study Period Start date Census date WW date End date
Semester 1 22/2/2021 23/3/2021 12/4/2021 30/5/2021

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2021 are indicative and subject to change. Finalised census dates for 2021 will be available from the 1st October 2020. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

  • analyse the problems and challenges associated with building distributed computing applications;
  • adapt the emerging big data and cloud technologies informed by an appropriate evaluation process, to support business applications.
  • Design high performance and cloud applications to support scalable online services.
  • Design big data processing applications to efficiently process high volume and velocity data
Field of Education Commencing Student Contribution 1 Grandfathered Student Contribution 1 Approved Pathway Course Student Contribution 2 Domestic Full Fee
029999 $993.00 $993.00 not applicable $2,402.00
  • Available as a Commonwealth Supported Place
  • HECS-HELP is available on this unit, depending on your eligibility3
  • FEE-HELP is available on this unit, depending on your eligibility4

1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.



KIT205 OR KIT206 OR KIT214


Teaching Pattern

Lectures: 3 hr/wk 
Tutorials: 2 hr/wk

AssessmentTutorial work (10%)|Examination - invigilated (externally - Exams Office) (50%)|Multithreaded Server (10%)|Cloud Based Processing System (20%)|Big Data Assignment-Hadoop (10%)
TimetableView the lecture timetable | View the full unit timetable



Required readings will be listed in the unit outline prior to the start of classes.

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