cse5bdc big data management on the cloud

BIG DATA MANAGEMENT ON THE CLOUD

CSE5BDC

2020

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 and Microsoft. We will study how to use the cloud services provided by these vendors to meet the big data needs of businesses. This subject includes 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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorZhen He

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsNo

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesCSE4OOF AND CSE4DBF

Co-requisitesN/A

Incompatible subjectsCSE4BDC OR CSE3BDC

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Learning resources

Hadoop in Action

Resource TypeOther resource

Resource RequirementRecommended

AuthorChuck Lam

Year2010

Edition/VolumeN/A

PublisherManning

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Hadoop The Definitive Guide

Resource TypeOther resource

Resource RequirementRecommended

AuthorTom White

Year2015

Edition/VolumeN/A

PublisherO'Reilly Media

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

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

02. Identify the best type of cloud-based service to use for range of application scenarios.

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

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

05. Setup cloud-hosted database systems.

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.

07. Analyse complex cloud computing architectures and propose remedies to any identified flaws.

Subject options

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Bendigo, 2020, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment informationN/A

Subject Instance Co-ordinatorZhen He

Class requirements

Computer LaboratoryWeek: 10 - 22
One 2.00 hours computer laboratory per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	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.	N/A	N/A	N/A	No	10	SILO3, SILO4, SILO5
One 3-hour examination (equivalent to 3000 words)	N/A	N/A	N/A	No	60	SILO1, SILO2, SILO3, SILO4, SILO6, SILO7
Programming Assignment (equivalent to 2500 words) The students are required to achieve greater than 50% for the non-exam components (including lab reports) as a hurdle	N/A	N/A	N/A	Yes	30	SILO3

Melbourne (Bundoora), 2020, Semester 1, Day