CC5062 - Data Engineering (2023/24)
| Module specification | Module approved to run in 2023/24 | ||||||||||
| Module title | Data Engineering | ||||||||||
| Module level | Intermediate (05) | ||||||||||
| Credit rating for module | 15 | ||||||||||
| School | School of Computing and Digital Media | ||||||||||
| Total study hours | 150 | ||||||||||
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| Assessment components |
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| Running in 2023/24(Please note that module timeslots are subject to change) |
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Module summary
This module provides an understanding of data engineering concepts, techniques and tools. It covers the basics of data modelling, storage, retrieval, and processing for data analysis needs. The module aims to provide a set of building blocks through which a complete architecture for modelling, storing and processing data can be constructed. It aims to enable students to apply the practical skills of data engineering techniques in the real world.
The aims of this module are to:
• provide students with an understanding of data engineering concepts and techniques
• enable students to appreciate various modern data engineering tools
• enable students to acquire fundamental knowledge and skills of data modelling, storage, retrieval, and processing for data analysis
• develop students with practical skills in applying tools and techniques to solve real world problems
Syllabus
• Concepts and fundamentals of data engineering
• Data engineering key skills and tools:
- Linux
- SQL vs NoSQL, Graph database
- Data Warehouse vs data lake
- Star Scheme
- Python Data Frame
- Stream processing with Kafka
- Apache Spark
- Big data and Hadoop platform
- Cloud
- DAMA
• Workflow of data engineering with ETL processing
- Collecting raw data
- Transforming data for data analysis needs
- Loading data and scheduling tasks
• Work through case studies
Learning Outcomes LO1 - LO5
Balance of independent study and scheduled teaching activity
Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:
- Lecture (1 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies
- Workshop (2 hour / week):
Data Engineering technical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with relevant packages.
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- Blended learning:
Using the University’s VLE and online tools to provide deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.
Students will be expected and encouraged to produce reflective commentaries on the learning activities and tasks that they carry out to complete their work.
Learning outcomes
On successful completion of this module the student should be able to:
[LO1] demonstrate a clear understanding of the key concepts and frameworks of data engineering
[LO2] gain a practical knowledge of various modern data engineering tools and techniques
[LO3] gain knowledge and skills of data modelling, storage, retrieval, and processing for data analysis
[LO4] develop an awareness of the latest developments in data engineering
[LO5] apply data engineering techniques and tools to solve real-world problems as part of a team
Assessment strategy
The module will be assessed by a practical piece of coursework (100%)
The coursework is designed to assess knowledge and practical skills of the module. It will provide students with the opportunity to undertake research on current issues and practical techniques in data engineering and its effective application. It will also enable students to work as a member of a development team to apply their knowledge to a practical business problem, demonstrating their skills for problem-solving and critical thinking and evaluation. LO1, LO2, LO3, LO4,LO5
Bibliography
Reading list available at: https://rl.talis.com/3/londonmet/lists/26C76F68-8052-DD45-3680-17BD2FC893D8.html?lang=en-US