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Case Study of Global Entertainments

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COURSEWORK ASSESSMENT SPECIFICATION

 

Module Title: Big Data and Cloud Security
Module Number: LD7081
Academic Year: 2019-2020
% Weighting (to overall module): 100%
Coursework Title: Report
Average Study Time Required by Student: 60 study hours

Dates and Mechanisms for Assessment Submission and Feedback

Date of Handout to Students:
Mechanism for Handout to Students:

ELP, discussed during lecture

Date and Time of Submission by Student:

 

Mechanism for Submission of Work by Student:

A word-processed document on ELP via Turnitin

Date by which Work, Feedback and Marks will be returned to Students:

Within twenty working days after the submission date.    

Mechanism for return of assignment work, feedback and marks to students:

Formal feedback will be made available via Blackboard following completion of all reviews and internal moderation of results.

 

Module Learning Outcomes (MLOs) assessed:

 

  1. Critically evaluate the principles, methods and tools of big data and cloud security to develop in-depth knowledge of key features and architectures.
  2. Demonstrate a critical understanding of Cloud Data Storage Architectures, Cloud software assurance, and audit process methodologies.
  3. Systematically design, critically evaluate and implement the security controls necessary to ensure confidentiality, integrity and availability in cloud and big data environment.
  4. Critically analyse and appraise knowledge of commercial/legal/social/ethical requirements and unique risks within a Cloud and Big data environment

 

General Information

This assignment constitutes 100% of the final mark for this module. Any queries relating to this assignment should be directed to:

 

 

Type of the submission required

This is an INDIVIDUAL piece of work contributing towards the module assessment. Deliverables should be assembled into a single report document, which includes (critical analysis, research, and snapshot evidences of tasks carried out and justification of design methods and technologies used). Submission will be in the form of an MS word report (4000 words).

 

Case Study

 

Global Entertainments (GE) is an entertainment company established in 1995 which has grown into a company with a turnover of £10million.  Its headquarters is in Slough, where the company was originally founded.  There are also regional offices in London, New York and Singapore.

GE maintains a centralised database in Slough which supports a number of transaction processing systems. Recently the management team focussed its attention on improving the decision-making capabilities of the organisation. In particular they wanted to provide regional managers with information and insights into existing data to enable more efficient decision making.   At present, database (RDBMS) is updated continually throughout the day.  For example, entertainment products (online film streaming/DVDs) are constantly requested by the customers and the online transaction processing system records and updates inventory records accordingly.

During the sales/promotions phase, regional managers often need online analysis reports to monitor sales performance in order to rectify actions for any deviations in performance. They also need timely analysis reports to assist in making long-term decisions. Currently, a great deal of effort is spent on collecting data from various systems before any analysis can be undertaken. Managers want and need more information, but analysts can provide only minimal information at a high cost within the desired timeframes. It is clear that in order to provide the necessary information more efficiently, there is a need to move the IT infrastructure to a cloud environment and use big data for this purpose.  However, there is a concern that this will cause some issues with security.

 

You have been hired as a Big Data Cloud Engineer to migrate the company’s centralized database (RDBMS) to Big Data in the Cloud and identify security vulnerabilities and potential security threats caused by migrating the company’s data to cloud and big data environments. You should also suggest ways of protecting the company from these dangers.

 

Section A (60%) – suggested word limit for this section is 2800 words

 

Tasks:

  1. Discuss and analyse the principles of Big Data in the Cloud – how this methodology can be helpful for Global Entertainments company (case study) – Suggested word limit for this task is 1000 words [Marks 20]

 

  1. Critically evaluate and discuss the security risks within IAAS, PAAS and SAAS and decide which one is more suitable and secure for the Global Entertainments company (case study) – Suggested word limit for this task is 1000 words [Marks 15]

 

  1. Critically evaluate and discuss Big Data tools, solutions and security in the Cloud Suggested word limit for this task is 800 words [Marks 20]

 

  1. Produce a high-level infrastructure diagram (your diagram should show the tools/solutions) [Marks 5]

 

Section B (40%) – Implementation – suggested word limit for this section is 1200 words

 

Tasks:

  1. Create a Big Data Project in the Cloud environment by migrating existing database (RDBMS) into Big Data in the Cloud environment based on the features chosen for Global Entertainments (section A) [Marks 20]

 

  1. Using the appropriate tools/software demonstrate security controls used for the above Big Data project [Marks 10]

 

  1. Discuss which professional practices can help the Global Entertainments to comply with relevant legal, ethical and social issues within the Cloud and Big data environment.

Suggested word limit for this task is 1200 words [Marks 10]

 

 

 

 

 

 

 

 

 

Academic Integrity Statement: You must adhere to the university regulations on academic conduct. Formal inquiry proceedings will be instigated if there is any suspicion of plagiarism or any other form of misconduct in your work. Refer to the University’s Assessment Regulations for Northumbria Awards if you are unclear as to the meaning of these terms. The latest copy is available on the University website.

https://northumbria-cdn.azureedge.net/-/media/corporate-website/new-sitecore-gallery/services/academic-registry/documents/qte/assessment/guidance-for-students/pl,-d-,005-v003-academic-misconduct-policy.pdf?modified=20190605171211&la=en&hash=A55A56D5BAD5746FC530D31C6291B10F861275CE

(Last accessed on 12th August 2019)

 

 

Formative Feedback

There will be an opportunity for formative feedback during the semester. You are advised to start working on this assignment as early as possible so that you can seek clarification from the module tutor regarding any questions you might have during the semester. Note that tutors will not predict your grade, and you should not take the lack of comment on any aspect of your work as indicating that it is correct. You should make every effort to take advantage of formative feedback as tutors will not comment on draft work at other times. Remember that you will get more useful feedback from us by asking specific questions than just presenting us with your documentation and asking, ‘Is this right?’

 

Penalties for Exceeding Word Limits:

The following penalties will be applied after any reductions in mark due to late submission have been made, Penalties will be applied as defined in the University Policy on Word Limits Policy.https://www.northumbria.ac.uk/-/media/corporate-website/new-sitecore-gallery/services/academic-registry/documents/qte/assessment/guidance-for-students/word-limits-policy.pdf?la=en&hash=D06E866BA9C788D7B1FD8EE3E7E3F34026CE9673 (accessed on 12th August 2019)

 

The actual word count is to be declared on the front of the assessment submission.

 

 

 

Late Submission Policy:

For coursework submitted up to 1 working day (24 hours) after the published hand-in deadline without approval, 10% of the total marks available for the assessment (i.e.100%) shall be deducted from the assessment mark. Penalties will be applied as defined in the University Policy on the Late submission work.

https://northumbria-cdn.azureedge.net/-/media/corporate-website/new-sitecore-gallery/services/academic-registry/documents/qte/assessment/guidance-for-students/late-submission-of-work-and-extension-requests-policy_v2.pdf?modified=20190619071234&la=en&hash=C112B22E14B9075D4E92C6A5842088F7921BAAA4

(Last accessed on 12th August 2019)

 

 

For clarity: a late piece of work that would have scored 65%, 55% or 45% had it been handed in on time will be awarded 55%, 45% or 35% respectively as 10% of the total available marks will have been deducted.

 

Failure to submit: The University requires all students to submit assessed coursework by the deadline stated in the assessment brief.  Where coursework is submitted without approval after the published hand-in deadline, penalties will be applied as defined in the University Policy on the Late Submission of Work.

https://northumbria-cdn.azureedge.net/-/media/corporate-website/new-sitecore-gallery/services/academic-registry/documents/qte/assessment/guidance-for-students/late-submission-of-work-and-extension-requests-policy_v2.pdf?modified=20190619071234&la=en&hash=C112B22E14B9075D4E92C6A5842088F7921BAAA4

(Last accessed on 12th August 2019)

 

 

Grading Guidance

 

Distinction:

Excellent in-depth application and critical research on the processes and user requirements. Provide in-depth knowledge of how to design and evaluate the principles of Big Data for a given scenario.

Excellent in-depth understanding and evaluation of security risks within IAAS, PAAS and SAAS. Shows excellent understanding of Big Data project in the Cloud environment. Shows excellent research on legal, ethical and social issues

 

Commendation:

Shows good research on the processes and user requirements.  Provides good knowledge of how to design and evaluate the principles of Big Data in the Cloud environment for a given scenario. Shows good understanding and evaluation of security risks within IAAS, PAAS and SAAS. Shows excellent understanding of Big Data project. Shows satisfactory research on legal, ethical and social issues

 

Pass:

Provides basic understanding of the deliverables.  Provides end-to-end design and all requirements are met. Report has some errors and lacks adequate explanation. The robustness and correctness of the principles of Big Data is not explained thoroughly. Evidence of design is shown but inadequate explanation of the architecture

 

Fail:

Provides incomplete attempt or lacks substantial parts of the deliverables. Fails to demonstrate understanding of the concepts required to implement deliverables. Work lacks serious clarity and detail. There are several errors in the report.

 

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