FIT3182 Big data management and processing
Faculty of Information Technology
FIT3182 Big data management and processing is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2022 in Semester 1 at Clayton and Malaysia. It needs FIT2004 and (FIT2094 or FIT3171).
- Credit points
- 6
- Offered in 2022
- Semester 1
- Clayton, Malaysia
- Assessment
- Exam 60%
- and 4 other tasks
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT3182
Prerequisites
Pass these before you enrol.
After FIT3182
No unit lists FIT3182 as a prerequisite in the 2022 handbook.
Enrolment rules
Students should have an understanding of database concepts and SQL and Python programming background.
Overview
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 an 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; and velocity data processing, covering data streaming.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester | Malaysia | On campus |
Assessment
- Take home testOtherThreshold hurdle5%
- Class TestIn class testThreshold hurdle10%
- Document and stream data processingAssignmentThreshold hurdle20%
- FLUX ParticipationParticipationThreshold hurdle5%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
identify 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 big data streaming technologies.
Workload and teaching
- Lectures24 hours
- Laboratories24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Where it fits
FIT3182 is part of 2 areas of study in the 2022 handbook.
Contacts
- Unit Coordinators
- Dr Vishnu Monn
- Dr Adel Nadjaran Toosi
- Chief Examiners
- Associate Professor David Taniar
Common questions
What are the prerequisites for FIT3182?
You need FIT2004 and (FIT2094 or FIT3171) before you enrol. Enrolment rules also apply.
When is FIT3182 offered?
In 2022, FIT3182 runs in Semester 1 at Clayton and Malaysia.
How much work is FIT3182?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3182 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 4 other tasks.
Which majors and minors include FIT3182?
FIT3182 is part of Advanced computer science and Data science.