COMP47470 Big Data Programming

Academic Year 2023/2024

`Big Data’ refers to datasets that are too big, or change too quickly, for traditional data management and data processing approaches. Big Data has forced the field of data management to rethink some of its design concepts and architectural patterns. This module will walk the students through the complex set of concepts and projects that form the Big Data stack. Students will learn how to set up Big Data environments, how to use efficient data management operations and how to run algorithms – to the scale and speed required by Big Data datasets. Students will also be able at the end of this module to design and implement their own solutions to address Big Data problems.

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Curricular information is subject to change

Learning Outcomes:

On successful completion of this module the learner will be able to:
- Understand the problem of managing data at scale and why traditional data management systems are failing
- Understand the various data management paradigms used in the context of Big Data (e.g., relational, NoSQL)
- Understand the role of distributed file systems and how to manage your own cluster (e.g., using HDFS)
- Understand Big Data programming models such as Map/Reduce and Spark, and how to use them on real examples
- Understand how graph processing is done on big graphs (e.g., using Giraph)
- understand how to process big data streams (e.g., using Storm)

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Practical

24
Autonomous Student Learning

62
Total

110
Approaches to Teaching and Learning:
Weekly quizzes, projects 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: End of trimester Exam 2 hour End of Trimester Exam No Graded No

40
Continuous Assessment: < Description > Throughout the Trimester n/a Graded No

60

Carry forward of passed components
Yes
 
Remediation Type Remediation Timing
Repeat Within Two Trimesters
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

solutions and feedback to weekly quizzes, and to projects

Name Role
Nils Hohing Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
      
Practical Offering 1 Week(s) - Autumn: All Weeks Fri 14:00 - 15:50
Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 12 Tues 11:00 - 12:50
Lecture Offering 1 Week(s) - 8, 10, 11 Tues 11:00 - 12:50
Autumn
      
Spring
      
Practical Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 12:00 - 13:50
Lecture Offering 1 Week(s) - 20, 21, 25, 31 Tues 11:00 - 12:50
Lecture Offering 1 Week(s) - 22, 23, 24, 30, 32, 33 Tues 11:00 - 12:50
Lecture Offering 1 Week(s) - 26, 29 Tues 11:00 - 12:50
Practical Offering 1 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 11:00 - 12:50
Spring