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Unit information: Large-Scale Data Engineering in 2021/22

Unit name Large-Scale Data Engineering
Unit code EMATM0051
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Mr. Forsyth
Open unit status Not open




School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description including Unit Aims

This unit aims to give a comprehensive overview of elastically scalable and remotely-accessed "cloud" computing services such as those offered by Amazon, Google, and Microsoft, and associated technologies for dealing with very-large-scale bodies of data.

The unit commences with discussion of the economics that have driven the rapid development and adoption of cloud computing in a variety of industries; it then explores the provisioning of cloud services moving from infrastructure-as-a-service (IaaS), through platform-as-a-service (PaaS), software-as-a-service (SaaS), and "serverless" functions-as-a-service (FaaS). The open-source Hadoop "ecosystem" cloud service projects is introduced, and various cloud data-storage and data-processing technologies are surveyed, with evaluation of their strengths and weaknesses. The unit closes with an overview of best practices in the use and management of Big Data.

Intended Learning Outcomes

On successful completion of the unit, students will be able to:

  1. Explain the economic factors and economies of scale that have driven the development of cloud computing;
  2. Compare and appropriately select among the various cloud computing services offered by major providers such as Amazon, Google and Microsoft, and have direct experience of initiating, running and managing, and closing remotely accessed computational resources via X-as-a-Service access models;
  3. Demonstrate competence as a practitioner of cloud computing architecture with reference to fundamental concepts such as availability, reliability, scalability, elasticity, security, cost effectiveness and automation;
  4. Demonstrate the combination and use of cloud computing technologies such as in-memory compute and stream-processing in high-performance and high-throughput applications;
  5. Apply effective methods to store, manage, process and secure data at very large scale (‘Big Data’).

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, group work, practical activities and self-directed exercises.

Assessment Information

Coursework 1: Design, implement and optimise an effective cloud architecture for an existing data processing application. (ILO 1-5; 100%)


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How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.