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Curricular information is subject to change
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 Type | Hours |
---|---|
Lectures | 24 |
Practical | 24 |
Autonomous Student Learning | 62 |
Total | 110 |
Not applicable to this module.
Resit In | Terminal Exam |
---|---|
Summer | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
solutions and feedback to weekly quizzes, and to projects
Lecture | Offering 1 | Week(s) - 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31 | Tues 14:00 - 14:50 |
Lecture | Offering 1 | Week(s) - 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31 | Wed 12:00 - 12:50 |
Practical | Offering 1 | Week(s) - 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31 | Wed 14:00 - 15:50 |