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Unit information: Cloud Computing and Big Data (Teaching Unit) in 2020/21

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Cloud Computing and Big Data (Teaching Unit)
Unit code COMSM0072
Credit points 0
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Schien
Open unit status Not open

COMS10012 Software Tools or equivalent.

Competent ability to use Unix/Linux command-line interface, and some experience of shell-scripting.

Basic knowledge of Python


EITHER Assessment Units COMSM0071 Cloud Computing and Big Data (Exam assessment, 10 credits)

OR COMSM0070 Cloud Computing and Big Data (Coursework assessment, 20 credits).

Please note:

COMSM0072 is the Teaching Unit for the Cloud Computing and Big Data option.

Single Honours Computer Science students can choose to be assessed by either examination (10 credits, COMSM0071) or coursework (20 credits, COMSM0070) by selecting the appropriate co-requisite assessment unit.

Any other students that are permitted to take the Cloud Computing and Big Data option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMSM0071).

School/department Department of Computer Science
Faculty Faculty of Engineering


This unit aims to give a comprehensive overview of elastically scalable and remotely-accessed "cloud" computing services such as those offered by Amazon, Google, Microsoft, and Oracle. and associated technologies. The unit commences with discussion of the economics that are driving 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 on to functions-as-a-service (FaaS, or "Serverless" cloud computing).

The first part of the unit covers core cloud technologies and services, and teaches contemporary cloud-based software development practices such as containerization, microservice architectures, and cloud orchestration. The second part of the unit covers core cloud-based software systems for managing, manipulating, and analysing large-scale data (what is colloquially known as "big data"). The unit closes with discussion of current research issues.

Intended learning outcomes

General ILOs

By the end of the unit students will be able to:

  1. Compare the 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.
  2. Describe and/or use key cloud technology developments such as distributed file systems and the MapReduce architecture, and the Hadoop "ecosystem" of open-source projects.
  3. Show knowledge of the range of cloud datastore/database and associated processing technologies options such "NoSQL", Graph Databases, and "NewSQL", and select a technology appropriate to a specific problem or application.

In addition to the general ILOs above, when assessed by examination, students will be able to:

  1. Demonstrate an understanding of the economic factors and economies of scale that have driven the development of cloud computing.
  2. Be familiar with at least one case-study of a contemporary successful company whose business model is dependent on cloud services.
  3. Be able to discuss current research issues in cloud computing.

In addition to the general ILOs above, when assessed by coursework, students will have an:

  1. Ability to showcase a scalable cloud application that they have written, made available via a public repository such as GitHub.

Teaching details

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

Teaching will take place over Weeks 1-7, with coursework support in weeks 8-10 and for students assessed by examination, consolidation and revision sessions in Weeks 11 and 12..

Assessment Details

Examination details:

January timed assessment (100%, 10 credits).


Coursework details:

Coursework (100%, 20 credits), to be completed over weeks 8-11.

Reading and References

  • Barrosso, L., Holze, U. and Ranganathan, P., The Data-Center as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Third Edition (Morgan & Claypool, 2018)
  • Chambers, B. and Zaharia, M, Spark: The Definitive Guide (O'Reilly, 2018)
  • Kleppmann, M., Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (O'Reilly, 2016)
  • Perkins, L. et al, Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement, Second Edition (O'Reilly, 2018)
  • Weinman, J., Cloudonomics (John Wiley, 2012)
  • White, T., Hadoop: The Definitive Guide (O'Reilly, 2015)
  • Wittig, M. and Wittig, A., Amazon Web Services in Action, Second Edition (Manning Publications, 2018)