Big Data Architecture and Management
Testimonials
Aleksandra Shcherbakova
Master of Management
“I have met some truly impressive talents and kind personalities among our lecturers."
Angelica Balajadia
Master of Management
“I have found my people here, I have networked here. I am open to doors opening and so many doors are opening already."
Wiranpatchara (Sandy) Wongchanapai
Master of International Business
"Whilst studying at ICMS, I was able to have a strong work/study life balance".
Cameron Colvin
Bachelor of Business (Marketing)
"My work experience boosted my confidence and allowed me to apply theoretical knowledge in practical settings."
Taylor McLeod
Bachelor of Business (Sports Management)
“The small, intimate environment in my lectures makes me feel like I am being heard and that I am getting the most out of my studies."
Julie Williams
Bachelor of Business (Fashion and Global Brand Management)
"I chose ICMS because they have provided me with the opportunity to kickstart my career through their many industry partners".
Keegan Du Preez
Bachelor of Business Management (Accounting)
"ICMS is realistic. They understand the realistic expectations of various markets. It’s not just textbook knowledge, especially compared to every other competitor".
Michaela Mayes
Bachelor of Business (Marketing)
"ICMS' Work Integrated Learning gave me the ability to gain real life experience before I even graduated".
Alana Williams
Bachelor of Business (Accounting)
“I worked full time whilst studying, with ICMS allowing for such a great work to study ratio and balance, where I was flexibly allowed to do both.”
Juliette Wilson
Bachelor of Business (Marketing)
"I think the biggest thing is that you get a personalised experience, and you’ll be treated like a person, not a number".
Calum McKnight
Bachelor of Business (Marketing)
“Very quickly, ICMS gave me a circle of friends who are pretty driven – but who also like to have fun – and when I see where they are now it’s pretty exciting and it drives me further.”
Ewan Metcalfe
Bachelor of Business (Sports Management)
"Through being at this college and the geographical location, it always reminded me to stay grounded, keep working hard to further improve my life and stay happy".
Big Data Architecture and Management is available as a postgraduate-level subject offered by the International College of Management, Sydney (ICMS). Please click the button below to find a postgraduate course.
Subject Code:
DAT801A
Subject Type:
Specialisation
Pre-requsite:
- DAT701A Analytics for Business Intelligence
- ICT701A Software Design and Construction
- Course level study pre-requisite: a total of 16 credit points (4 subjects) prior to enrolling into the subject.
Subject Level:
800
Credit Points:
4 credit points
Subject Aim:
Given the increasing digitalisation, massive amounts of structured and unstructured data are generated across various sources. This phenomenon of constantly growing data that is extremely complex in structure is referred to as Big Data. Traditional databases are not suitable for efficient indexing, sorting, searching, analysing, and visualising of Big Data. To be able to use this data to gain deeper insights and, thus, leverage a competitive advantage, organisations need the competencies to manage Big Data and to apply Big Data technologies and techniques effectively.
This subject provides students with an introduction to Big Data, its 5-V characteristics (Volume, Velocity, Veracity, Variety, and Value), resulting challenges for organisations, and state-of-the-art technologies and techniques such as NoSQL, parallel and distributed memory systems, and streaming models. It introduces key concepts on how to create business value from Big Data and consequently empowers students to apply their Big Data expertise to develop business solutions.
In this subject, students will study Big Data not only from a theoretical or technical perspective, but they will also learn to master the challenges of Big Data in a complex business context. This includes studying architectures, systems, and management techniques.
Learning Outcomes:
a) Communicate knowledge about Big Data characteristics, challenges for traditional database systems, and state-of-the-art Big Data architectures and techniques to professional audiences.
b) Develop requirements and contextualise Big Data concepts to complex use cases.
c) Critically evaluate and select distributed storage and computation techniques to manage Big Data and draw value from it.
d) Design and implement a prototypical Big Data solution to a complex business problem by applying advanced data storage, processing, querying and analysis tools.
e) Plan and develop contemporary Big Data solution, critically analyse risks and opportunities, and effectively communicate recommendations to various stakeholders.
Assessment Information:
Learning outcomes for this subject are assessed using a range of assessment tasks as described in the table below.
| No | Assessment task | Weighting | Subject learning outcomes to be assessed |
| 1 | Online Quiz (Invigilated) | 20% | a, b |
| 2 | Big Data Project (G) | a, b, c, d, e | |
| Part A) Report | 20% | ||
| Part B) Pitch | 20% | ||
| 3 | Big Data Prototype Solution | ||
| Part A) Prototype | 25% | a, b, c, d, e | |
| Part B) Video Presentation | 15% |
Broad topics to be covered:
Week 1: Introduction to Big Data
|
Week 2: Tradition Databases vs. Big Data
|
Week 3: Enterprise Architecture Management and Big Data
|
Week 4: MapReduce Programming Model
|
Week 5: MapReduce Programming Model
|
Week 6: Hadoop Platform for Storing and Processing Big Data
|
Week 7: Spark Platform for Storing and Processing Big Data
|
Week 8: Data Streaming: Introduction
|
Week 9: Data Streaming: Sampling
|
Week 10: Processing Big Data with Scala
|
Week 11 Current Trends and Future Potentials of Big Data
|
Please note that these topics are often refined and subject to change so for up to date weekly topics and suggested reading resources, please refer to the Moodle subject page.