Module Details

Module Code: DATA8002
Title: Data Management Systems
Long Title: Data Management Systems
NFQ Level: Advanced
Valid From: Semester 2 - 2021/22 ( January 2022 )
Duration: 1 Semester
Credits: 5
Field of Study: 4816 - Data Format
Module Delivered in: 4 programme(s)
Module Description: This module introduces students to the use of database management systems for applications. It includes an evaluation of the relational model and NoSQL data models, and how to query and manipulate data stored using these models. Students will learn how these data models are used in the distribution of data and the emerging "Big Data" paradigm.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Explain the concepts of Database Management Systems and Data Models, such as Relational and NoSQL
LO2 Implement and query relational databases using SQL Data Definition and Manipulation commands
LO3 Evaluate the suitability of data models for a given data management requirement
LO4 Devise solutions to NoSQL database queries using interactive commands
LO5 Compare and contrast approaches to the distribution of data
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
Traditional Database Systems Concepts
DBMS concepts: Data Integration and sharing, comparison with traditional data processing systems; DBMS architectures; Data Independence; The Relational Data Model.
Structured Query Language
Manipulating data in SQL; Processing Single & Multiple Tables - SELECT commands. Functions & Group By; Database Definition in SQL - CREATE, DROP, ALTER, CHECK commands.
NoSQL Systems
Motivation for NoSQL Data Models and Systems; Types of NoSQL systems / data models: MapReduce framework, Key-value stores, Document stores, Graph database systems. Creating and querying NoSQL Systems.
Distributed Databases
Sharding; Master-Slave and Peer-to-Peer Replication; Distributed Filesystems; The Big Data Paradigm.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 25
Timing Week 7 Learning Outcomes 1,2
Assessment Description
SQL data definition and manipulation
Assessment Type Practical/Skills Evaluation % of Total Mark 25
Timing Week 11 Learning Outcomes 1,4
Assessment Description
Creating and manipulating data in a NoSQL system
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 1,2,3,4,5
Assessment Description
Project examining all aspects of the module. An example is a large NoSQL project, including data and queries. It will be submitted by students and will be assessed as a report.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

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 Theory Every Week 2.00 2
Lab Contact Lab Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Independent study and project work 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 Theory Every Week 2.00 2
Lab Contact Lab Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Independent study and project work 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
  • Thomas M. Connolly, Carolyn E. Begg. (2014), Database systems: A Practical Approach to Design, Implementation and Management, 6th. Pearson, [ISBN: 9781292061184].
  • Pramod J. Sadalage, Martin Fowler. (2012), NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, [ISBN: 978-0321826626].
Supplementary Book Resources
  • Eric Redmond, Jim Wilson. (2012), Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement, [ISBN: 978-1934356920].
  • Mukesh Negi. (2019), Fundamentals of Database Management System: Learn essential concepts of Database Systems, BPB Publications, [ISBN: 9789388176620].
Supplementary Article/Paper Resources
  • Codd, E. F.. (1970), A Relational Model of Data for Large Shared Data Banks, Communications of the ACM, 13:6, p.377-387.
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KCMSD_8 Higher Diploma in Science in Cloud & Mobile Software Development 1 Mandatory
CR_KCLCO_8 Higher Diploma in Science in Cloud Computing 1 Mandatory
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory
CR_SDAAN_9 Master of Science in Data Science & Analytics 1 Mandatory