Big Data Paradigms: Database Concepts, Storage Solutions & Insights

Coursework 1 25% Overview

• Occurs:
• Campus-based, in Week 7. Specific dates are to be announced this week.
• Feedback Date:
• Within University guidelines, 20 working days after submission.
• Related learning outcomes:
• Demonstrate how a variety of database/data storage paradigms may be applied to address the challenges it presents.
• Students will be expected to complete a 60-minute, online test.
• This test will assess understanding of concepts which have been introduced and detailed until that point.

• The topics to cover are:
• General Database Concepts
• Relational Databases
• NoSQL Concepts
• Document Databases
• Timeseries Databases
• Graph Databases
• Coursework 1 will be delivered, submitted and assessed through the Blackboard online learning environment.
• This is a closed-book assessment.

• Occurs:
• Presentation Slides and Video Demonstration, there will be links for the two uploads.
• Submission Deadline:
• To be updated
• Feedback Date:
• Within 20 working days from submission, as per University Policy

• Assess the concepts of various database and data storage paradigms, and determine their appropriate applications for big data challenges.

• Identify and analyse shortcomings in your interactions with different technologies, using these insights to improve future practices.

• Select and apply skills to effectively utilise data from diverse database and storage paradigms.

• Identify and evaluate datasets on educational attainment and nutritional quality from sources like Kaggle and Data.gov.
• Select appropriate datasets that match your interests.
• Integrate them into a data lake and explain your choices.
• Analyse the data for useful insights and visualise the results using relevant data lake technologies.

• Students are expected to produce:
• Presentation slides in PowerPoint
• A 5-minute demonstrative video, demonstrating the solution.
• NOTE: The video is to demonstrate the solution. It is not to be a recording of the slides being presented.

The demonstration should cover:

• Why the big data technology
• How the data were stored
• How the data were analysed
• The insights from the data
• The visualisations used

NOTE: The video demonstration should be in an acceptable format and there must also be an audio explaining your steps.

• Slide 0. Title Slide. (0%)
• Slides 1 - 3. Discussion of the problem and justification of the dataset (15%). Why your dataset(s)?
• Slides 4 - 8. Overview of the technical solution developed (25%). Details of your solution including a flowchart.
• Slides 9 - 12. The analysis performed, and insight obtained (20%). Understandable insights and metrics to measure insights.
• Slide 13. Concluding comments (5%). Summary of findings and insights
• Slide 14. References (5%). Always reference your work appropriately.
• Slide 15. Link to your video demonstration [5-minute video].

NOTE: Video Demonstration is 30% of Coursework 2 and must not exceed 5 minutes. Any video exceeding 10% of the stipulated time will be penalised.