FIT5148 - Big data management and processing - 2019

6 points, SCA Band 2, 0.125 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Information Technology

Chief examiner(s)

Associate Professor David Taniar

Unit guides

Offered

Caulfield

  • First semester 2019 (On-campus)

Malaysia

  • First semester 2019 (Evening)

Monash Online

  • Teaching Period 5 2019 (Online)

Prerequisites

(FIT9131 or FIT9133) and FIT9132

For students enrolled in E3001, E3002, E3005, E3010, E3011, E3007 completing the Software Engineering specialisation: FIT2099 and FIT3171

Students should have an introductory understanding of database concepts and SQL and some programming background.

Prohibitions

FIT5043

Notes

Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data ScienceGraduate Diploma in Data Science (http://online.monash.edu/course/graduate-diploma-data-science/?Access_Code=MON-GDDS-SEO2&utm_source=seo2&utm_medium=referral&utm_campaign=MON-GDDS-SEO2) via Monash Online.

Synopsis

Data engineering is about developing the software (and hardware) infrastructure to support data science. This unit introduces software tools and techniques for data engineering, but not hardware. It will cover:

  • introduction to big data processing, covering volume, variety, and velocity;
  • large volume data processing using parallel technologies;
  • variety data formats, including unstructured and semi-structured data, using NoSQL databases;
  • velocity data processing, covering data streaming;

Outcomes

On successful completion of this unit, students should be able to:

  1. identify and assess big data concepts and technologies;
  2. write and interpret parallel database processing algorithms and methods;
  3. use big data processing frameworks and technologies;
  4. describe and compare NoSQL technologies;
  5. use and evaluate streaming methods in big data processing;
  6. use big data streaming technologies.

Assessment

For Monash Online: In-semester assessment: 100%

On-campus: Examination (2 hours): 50%; In-semester assessment: 50%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

  1. Contact hours for on-campus students:
    • Two hours/week lectures
    • Two hours/week laboratories
  2. Contact hours for Monash Online students:
    • Two hours/week online group sessions.
    • Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.
  3. Additional requirements (all students):
    • A minimum of 8 hours per week of personal study (22 hours per week for Monash Online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

See also Unit timetable information

This unit applies to the following area(s) of study

Advanced data analytics

Data science