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 … For more content click the Read More button below.
Requisites
Prerequisite
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Associate Professor David Taniar
Learning outcomes
On successful completion of 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.
Assessment summary
Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%.
This unit contains hurdle requirements which you must achieve to be able to pass the unit. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.