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 plays 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.
This unit involves a lot of programming and implementation of medium-size real applications. Therefore, good programming skills are essential.
|Unit name||Big Data and Cloud Computing|
|Faculty/School||College of Sciences and Engineering
School of Technology, Environments and Design
|Discipline||Information & Communication Technology|
|Available as student elective?||Yes|
|Location||Study period||Attendance options||Available to|
- International students
- Domestic students
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
Special approval is required for enrolment into TNE Program units.
|Study Period||Start date||Census date||WW date||End date|
* 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 (see withdrawal dates explained for more information).
Unit census dates currently displaying for 2020 are indicative and subject to change. Finalised census dates for 2020 will be available from the 1st October 2019.
|Band||CSP Student Contribution||Full Fee Paying (domestic)||Field of Education|
|2||2020: $1,190.00||2020: $2,402.00||029999|
Fees for next year will be published in October. The fees above only apply for the year shown.
Please note: international students should refer to this page to get an indicative course cost.
- KIT214 or alternatively, a DN or HD in either KIT107 or KIT205.
- Note: students enrolling with the alternative pre-requisite need to be aware that there may be some concepts they will need to study by themselves to participate in the unit. If a student has not achieved the required marks in KIT107 or KIT205 then the student will need to email the Unit Coordinator approval.
You cannot enrol in this unit as well as the following:
3-hr lecture weekly, 2-hr practicals weekly
2-hr exam (50%), In-Semester (50%)(Assignments and Quizzes)
|Timetable||View the lecture timetable | View the full unit timetable|
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