Module Details

Module Code: SOFT8041
Title: Cloud Based Data Management
Long Title: Cloud Based Data Management
NFQ Level: Advanced
Valid From: Semester 2 - 2021/22 ( January 2022 )
Duration: 1 Semester
Credits: 5
Field of Study: 4814 - Computer Software
Module Delivered in: 1 programme(s)
Module Description: This module will develop the competencies to allow a student to develop requirements and implement a solution to a cloud based data management system for a smart product or service.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse and develop a set of cloud based data storage and management requirements for a smart product based data gathering product or service.
LO2 Develop a cloud suited data object model and or/database schema suitable to store and manage data gathered from a Smart product or IoT network.
LO3 Implement a cloud based data management system either through a virtualised server and/or via commercial cloud based infrastructure services.
LO4 Evaluate and test the operation of a cloud based data storage and management system with respect to initial requirements.
LO5 Demonstrate a critical understanding in the application of the legal, ethical and regulatory requirements of cloud based data management.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Data Representation and Actions
Data Types, Object Models, Storage Schemas(Relational, Time Based) as suited to remote and cloud storage.
Virtualisation
Personal Servers - Creation, application and use. Commercial Virtual Infrastructure services Eg., AWS, MS Azure. Cybersecurity implications of virtualisation.
Cloud Based Frameworks
Cloud based data management and development frameworks, services and applications, stream processing, anomaly detection, event/rule handling, command/data partitioning, cybersecurity provisions
Remote Distribution Impacts
Performance Management, Dashboards, Load Balancing of data volumes and bandwidth. Evaluation techniques, Issues.
Legal, Ethical, Regulatory Requirements
Business/legal requirements, GPDR, fair use, security of data, residency.
Module Content & Assessment
Assessment Breakdown%
Coursework50.00%
End of Module Formal Examination50.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 25
Timing Every Week Learning Outcomes 1,2,3,4
Assessment Description
Series of laboratories to explore the topics and provide concrete examples, with relevant technical exercises to each laboratory requiring handup. E.g. laboratories on (a) data models and databases(b) Cloud service examples. (c) Testing cloud services. (d) Cybersecurity Penetration Testing. Exercises on each.
Assessment Type Project % of Total Mark 25
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
Project to apply and implement elements of coursework. Project may be individual or team based, e.g. (a) create requirements for a specific product, (b) Develop a data model and (c) implement and test on a cloud based system.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 50
Timing End-of-Semester Learning Outcomes 1,2,5
Assessment Description
Formal examination to evaluate problem analysis and calculation capabilities of the student.
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Class/Lectures Every Week 2.00 2
Lab Contact Laboratory and project work Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Exercises and reading materials Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Class/Lectures Every Week 2.00 2
Lab Contact Laboratory and project work Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Exercises and reading materials Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Ian Foster & Dennis B. Gannon. (2017), Cloud Computing for Science and Engineering (Scientific and Engineering Computation), 1st. MIT Press, USA, p.392, [ISBN: 9780262037242].
Supplementary Book Resources
  • Amazon Web Services. AWS IoT: Developer Guide, 1. Amazon, USA, p.1338, [ISBN: B07JBRCWWZ].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_ESMPR_8 Bachelor of Engineering (Honours) in Smart Product Engineering 7 Mandatory