Module Specification

Modern Database Systems

London school of INNOVATION

Module Specification

Modern Database Systems: Master the Future of Data Management



In an era where data drives decision-making and innovation, understanding the underlying architecture of database systems is crucial. This module offers an in-depth exploration into data modelling, database structures, and advanced data management techniques, providing a comprehensive toolkit for future database professionals.

This module will delve into key concepts such as relational and non-relational databases, transaction management, and advanced querying mechanisms. You will learn about contemporary solutions like NewSQL and cloud-based platforms, as well as modern alternatives like GraphQL and in-memory databases. The scope also covers critical aspects like database security, backup and recovery strategies, and emerging technologies.

You'll benefit from practical knowledge that directly applies to real-world scenarios, gaining expertise that enhances your career prospects. With insights into current trends and future-proofing technologies, you will be prepared to tackle complex data challenges and contribute effectively in any data-centric role.


Code Number of Credits ECTS Credits Framework HECoS code
DS71 30 15 FHEQ - L7

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Analyse the characteristics and use cases of relational, non-relational, and emerging database technologies.
LO2 Knowledge and Understanding Critically evaluate data modelling techniques, including ERD and normalisation, within modern database systems.
LO3 Intellectual Skills Assess the implications of different transaction management approaches, including ACID properties and eventual consistency.
LO4 Intellectual Skills Formulate advanced strategies for implementing and managing a variety of database structures in real-world scenarios.
LO5 Professional/Transferable Skills Strategically evaluate the ethical and professional considerations in database management and decision-making processes.

Assessment Patterns

Weighting Format Outcomes assessed
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.
K LO1
K LO2
I LO3
I LO4
P LO5
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.
K LO1
K LO2
I LO3
I LO4
P LO5

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 36.00
AI formative assessment 18.00
Workshop/Lab Sessions 27.00
Independent reading, exploration and practice 153.50
Summative assessment 64.00
300.00

Content Structure

Week Chapter Name Chapter Description
Week 1 Data Modelling Understand the foundations of data modelling by exploring concepts like Entity-Relationship Diagrams (ERD). Learn why proper data modelling is critical for efficient database design and data integrity.
Week 2 Relational Databases Dive into relational databases (SQL) to comprehend their structure and use cases. Learn how to perform CRUD operations as well as essential querying techniques such as Joins, Aggregation, and Grouping. Learn why relational databases are reliable and how they ensure data integrity through ACID properties.
Week 3 NoSQL Databases Explore various NoSQL databases and their unique capabilities. Understand why they are preferred for specific use cases, especially for handling large volumes of unstructured data. Learn about primary and secondary indexes how to query data from popular NoSQL database engines.
Week 4 NewSQL Solutions Examine NewSQL databases that aim to bridge the gap between traditional SQL systems and NoSQL. Learn why they are emerging as potential solutions for scalability while maintaining transactional consistency.
Week 5 Cloud Databases Investigate cloud-based database services like AWS RDS, Google Cloud SQL, and Azure SQL. Understand why organisations are increasingly moving to the cloud for database solutions and the benefits and limitations of these services.
Week 6 Transaction Management Explore the principles of transaction management, including ACID properties for SQL databases and eventual consistency for NoSQL systems. Understand why robust transaction management is crucial for maintaining data reliability. Learn to implement data integrity and constraints using Primary and Foreign Keys, Referential Integrity, Indexes, Constraints, and Triggers.
Week 7 Database Scalability Learn about the challenges of scalability and strategies for vertical versus horizontal scaling.
Understand how distributed databases can be used for scaling data, and how to use modern techniques for replication, sharding, and partitioning. Explore Consistency, Availability, and Partition Tolerance (CAP Theorem) and the role of load balancing for high availability.
Week 8 Database Security Learn the essentials of database security, including authentication, authorisation, encryption, and access control. Understand common threats like SQL injection and insider attacks, and explore tools for monitoring and auditing to maintain data integrity and confidentiality.
Week 9 Emerging Technologies Stay abreast of current trends and technologies like graph databases, data lakes, and multi-model databases. Understand why it is vital to keep up with these emerging technologies for future-proofing database solutions. Explore how in-memory databases fill an essential gap.

Module References

There are no module reference contents to display.

Methods of teaching/learning


Introductory lecture (1.50 hours)

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.


Concept learning (knowledge graph) (36.00 hours)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.


AI formative assessment (18.00 hours)

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.


Workshop/Lab Sessions (27.00 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students. There will be an opportunity also for Q&A in every session.


Independent reading, exploration and practice (153.50 hours)

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

Programmes this module appears on

Programme Term Type
1 Data Science and Analytics (MSc) 1 Core
2 Digital Project Management (MSc) 2 Optional
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Modern Database Systems: Master the Future of Data Management (DS71)