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Course syllabus - Smart Digital Platforms: Cloud Computing, Security and Big Data

Scope

7.5 credits

Course code

DVA260

Valid from

Autumn semester 2026

Education level

First cycle

Progressive Specialisation

G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements)

Main area(s)

Computer Science

Organisation

Department of Computer Science & Engineering

Ratified

2020-12-15

Revised

2025-11-03

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2025-09-09
    • Latest update: 2025-09-09

    Books

    Ruparelia, Nayan

    Cloud computing

    ISBN: 9780262529099

    LIBRIS-ID: 19806677

    Cambridge, Massachusetts, The MIT Press, xiii, 260 pages

    Silberschatz, Abraham; Galvin, Peter Baer.; Gagne, Greg.

    Operating system concepts

    ISBN: 9781118063330

    LIBRIS-ID: 13898924

    9. ed., Hoboken, N.J., Wiley, xxii, 944 p.

    Marz, Nathan; Warren, James

    Big data: principles and best practices of scalable real-time data systems

    ISBN: 9781617290343

    LIBRIS-ID: 18715698

    Shelter Island, NY, Manning, xx, 308 p.

    Pfleeger, Shari Lawrence; Coles-Kemp, Lizzie

    Security in computing

    ISBN: 0137891377

    LIBRIS-ID: 0h989bt5xgrjhwl5

    URL: Link

    URL: Link

    URL: Link

    Sixth edition., Boston, Addison Wesley Professional, 1 online resource

    Matt Bishop,

    "Computer Security Art and Science, 2nd Edition"

    LIBRIS-ID: m6ld9q20k88fk8tl

    URL: Link

    URL: Link

    URL: Link

    URL: Link

    URL: Link

    URL: Link

    Articles

    Mell, P.; Tim, G.

    The NIST definition of cloud computing

    NIST,

    Liu, F.; Tong, J.; Mao, J.; Bohn, R.; Messina, J.; Badger, L.; Leaf, D.

    NIST cloud computing reference architecture

    NIST special publication. 500(2011):1-28

    NIST,

    Al-Dulaimy, A.; Sharma, Y.; Khan, M.G.; Taheri, J.

    Introduction to edge computing

    Edge Computing: Models, technologies, and applications. 2020:3-25. Not mandatory.

    Al-Dulaimy, Auday; et al,

    The computing continuum: From IoT to the cloud

    Internet of Things 27 (2024): 101272,

    Web adresses

    URL: CISCO

    Not mandatory.

    URL: IBM

    URL: Amazon

    Not mandatory.

    URL: Amazon

    Not mandatory.

    URL: Google

    Not mandatory.

    URL: GeeksforGeeks

Objectives

The purpose of this course is to introduce the concept of cloud computing and give knowledge and insights in handling and processing Big Data in the cloud. The course will provide students with the necessary theoretical and practical knowledge to be able to use cloud services in managing Big Data and taking into account the data security aspects.

Learning outcomes

After completing the course, the student shall be able to:

  1. describe the cloud models, service models, structure and functionalities,
  2. describe the concept of Big Data and relation to cloud services,
  3. differentiate between different database services and apply them in different scenarios,
  4. understand and compare different storage options,
  5. describe and recommend how data is secured in cloud environment and
  6. practically apply a number of cloud services to search large amounts of data.

Course content

  • Introduction to Cloud Computing: Concept, Architecture, Services, and Deployment models
  • Cloud technologies: Virtualization, Load Balancing, Scalability and Elasticity
  • Cloud storage: Overview of storage technologies
  • Cloud databases: Database concept and languages
  • Big Data in the cloud: Concept and analytic services
  • The de facto Big Data processing frameworks: Hadoop and MapReduce
  • Security and Privacy in the cloud: Concept and challenges
  • Cloud Access: authentication, authorization and accounting

Specific requirements

Programming 7,5 credits.

Examination

Written examination (TEN1), 2,5 hp, examines the learning outcomes 1-5, marks Fail (U), 3, 4 or 5.
Project (PRO1), 3 hp, examines the learning outcome 6, marks Fail (U), 3, 4 or 5.
Laboratory work (LAB1), 2 hp, examines the learning outcomes 2-4, marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course overlaps with 3 credits towards DVA453 Machine Learning with Big Data, with 4.5 credits towards DVA254 Cloud Services and with 2.5 credits towards DVA444 Industrial Systems in Cloud Computing.