Course Title: Big Data Infrastructures

Part A: Course Overview

Course Title: Big Data Infrastructures

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2636

City Campus

Postgraduate

140H Computer Science & Information Technology

Face-to-Face

Sem 1 2016

COSC2636

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017

Course Coordinator: Dr. Zhifeng Bao

Course Coordinator Phone: +61 3 9925 1940

Course Coordinator Email: zhifeng.bao@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Database systems: this prerequisite knowledge can be attained by completing ISYS1055 Database Concepts and COSC2407 Database Systems.

Extensive programming skills: this prerequisite knowledge can be attained by completing COSC1295 Advanced Programming
 


Course Description

This course builds on skills gained in database management systems and gives students an in-depth understanding of a wide range of fundamental Big Data processing platforms.

This course is an overview of Big Data tools and technologies. It establishes a strong working knowledge of the concepts, techniques and products associated with Big Data. The main focus is on the different storage models, processing approaches and reporting tools available to work with Big Data.

Students will learn the core functionality of each major Big Data component and how they integrate to form a coherent solution with business benefit. Hands-on exercises aim to provide insight into what the tools do so that their role in Big Data systems can be understood.

The course emphasizes on how to plan and implement a Big Data solution and the various technologies that comprise Big Data. Many examples and exercises of Big Data systems are provided throughout the course. There will be some exposure, although minimal, to programming examples. These examples will provide an understanding of the workings of the major components a Big Data solutions and how they can be integrated to solve different business problems.

The course keeps a good balance between algorithmic and systems issues. The algorithms discussed in this course involve methods of organising big data for efficient complex computation using MapReduce in particular platforms, such as Hadoop, to present practical applications for Big Data.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the development of the following Program Learning Outcomes:

Problem Solving:

Ability to model and implement efficient big data solutions for various application areas using appropriately selected tools and architectures.


Critical Analysis:

Ability to analyse big data infrastructures and their components, to compare and evaluate them, and make appropriate design choices when solving real-world problems.


Communication:

Ability to motivate and explain trade-offs in big data platform design and analysis in written and oral form.


On completion of this course you should have gained an understanding of Big Data concepts, including cloud and big data architectures, an overview of Big Data tools and platforms, and to apply these concepts using an industry standard tools and products.
The key learning outcomes are:

  • Understand the Big Data Fundamentals, including the evolution of Big Data, the characteristics of Big Data and the challenges introduced.
  • Understand how to use distributed file systems to accommodate Big Data, how to store and query Big Data using state of the art environments like Hadoop.
  • Understand MapReduce and its use in creating, distributing, monitoring and executing processing jobs in distributed storage environments.
  • Understand what are the issues in developing a Big Data Strategy, defining a Big Data strategy for an organisation, establishing Big Data needs, evaluating commercial Big Data tools as well as enabling analytic innovation and selecting the correct tools.


Overview of Learning Activities

Key concepts will be explained in lectures, classes or online, where syllabus material will be presented and the subject matter will be illustrated with demonstrations and examples. Tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide practice in the application of theory and procedures, allow exploration of concepts with teaching staff and other students, and give feedback on your progress and understanding; assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.


Overview of Learning Resources

You will make use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

Use the RMIT Bookshops textbook list search page to find any recommended textbook(s).


Overview of Assessment

The assessment for this course comprises of assignments and a formal written examination.

For standard assessment details, including deadlines, weightings, and hurdle requirements relating to Computer Science and IT courses see: http://www.rmit. edu.au/compsci/cgi