BIG DATA MANAGEMENT ON THE CLOUD

CSE5BDC

2018

Credit points: 15

Subject outline

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

Credit points15

Subject Co-ordinatorZhen He

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites CSE4DBF and CSE4IOO

Co-requisitesN/A

Incompatible subjects CSE3BDC and CSE4BDC

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
Discipline SpecificHadoop The Definitive GuideRecommendedTom White, 2015O'Reilly Media
Discipline SpecificHadoop in ActionRecommendedChuck LamManning

Graduate capabilities & intended learning outcomes

01. Explain the benefits of cloud computing over traditional methods for managing big data.

Activities:
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.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)

02. Identify the best type of cloud-based service to use for a particular application scenario.

Activities:
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.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)
Critical Thinking (Critical Thinking)

03. Write efficient map and reduce programs to analyze large data sets.

Activities:
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.
Related graduate capabilities and elements:
Creative Problem-solving (Creative Problem-solving)
Inquiry/ Research (Inquiry/ Research)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)

04. Write efficient programs that query cloud-hosted database systems.

Activities:
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.
Related graduate capabilities and elements:
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)

05. Setup cloud-hosted database systems.

Activities:
Students will practise setting up cloud-based database systems during the labs.
Related graduate capabilities and elements:
Discipline-specific GCs (Discipline-specific GCs)

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.

Activities:
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.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Inquiry/ Research (Inquiry/ Research)
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)

07. To analyse a given complex cloud computing architecture and to propose remedies to any identified flaws.

Activities:
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.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)

Subject options

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Start date between: and    Key dates

Melbourne, 2018, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorZhen He

Class requirements

Lecture Week: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Computer Laboratory Week: 11 - 22
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.

Assessments

Assessment elementComments% ILO*
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 examination60 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 hurdle30 03