BIG DATA MANAGEMENT ON THE CLOUD
Credit points: 15
In this subject we will study the current state-of-the-art technologies for analysing huge amounts of data and responding to millions of user requests within one second. Currently the most cost efficient way of achieving the above aim is to use large-scale cloud-based services offered by vendors such as Amazon, Google, IBM, Microsoft, etc. We will study how to use the cloud services provided by these vendors to meet the big data needs of businesses. In particular this subject will include the following topics: cloud architectures, parallel database systems, map and reduce, key value stores, transaction support in the cloud, virtualization, and multi-tenant database systems.
SchoolSchool Engineering&Mathematical Sciences
Subject Co-ordinatorZhen He
Available to Study Abroad StudentsYes
Subject year levelYear Level 5 - Masters
Prerequisites CSE4DBF and CSE4IOO
Incompatible subjects CSE3BDC and CSE4BDC
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Discipline Specific||Hadoop The Definitive Guide||Recommended||Tom White, 2015||O'Reilly Media|
|Discipline Specific||Hadoop in Action||Recommended||Chuck Lam||Manning|
Graduate capabilities & intended learning outcomes
01. Explain the benefits of cloud computing over traditional methods for managing big data.
- Lectures on the architecture of cloud-based systems in weeks 1 and 2. The students will practice this by answering questions during lectures and answering study questions.
02. Identify the best type of cloud-based service to use for a particular application scenario.
- Lectures in week 2 and 3 on the three types of cloud-based services including: infrastructure as a service, platform as a service and software as a service. The students will practice this by answering questions during lectures and answering study questions.
03. Write efficient map and reduce programs to analyze large data sets.
- The students will practise answering questions during lectures in week 4 and 5. The students will practise programming map and reduce in the labs and during the programming assignment.
04. Write efficient programs that query cloud-hosted database systems.
- The students will practise answering questions during lectures in week 6. The students will practise writing cloud-hosted database queries in the labs and during the programming assignment.
05. Setup cloud-hosted database systems.
- Students will practise setting up cloud-based database systems during the labs.
06. Identify advantages and disadvantages of state-of-the-art technologies developed by research projects in the area of big data analytics using cloud-based services.
- Lectures in weeks 10 to 12 on advanced techniques for big data analytics. The students will practise this via practise questions during lectures and take home study questions.
07. To analyse a given complex cloud computing architecture and to propose remedies to any identified flaws.
- Students will be given examples of complex cloud architectures in lectures 10 and 11. They will be shown potential flaws and how these can be addressed. In lecture 12 students will work in groups to analyse a complex architecture and provide their assessment of any potential flaws and remedies for the flaws.
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Melbourne, 2019, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorZhen He
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
One 2.0 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
|10 Laboratory Reports (equivalent to 1000 words)||Each lab report is equivalent to a 100 word essay amount of work. The lab reports will be marked and returned to the students before the start of the following lab.||10||03, 04, 05|
|One 3-hour examination||60||01, 02, 03, 04, 06, 07|
|Programming Assignment (equivalent to 2500 words)||The students are required to achieve greater than 50% for the non-exam components as a hurdle||30||03|