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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 | 18 |
| Practical | 18 |
| Autonomous Student Learning | 64 |
| Total | 100 |
Not applicable to this module.
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Continuous Assessment: < Description > | Throughout the Trimester | n/a | Graded | No | 100 |
| Resit In | Terminal Exam |
|---|---|
| Summer | Yes - 1 Hour |
• Group/class feedback, post-assessment
• Online automated feedback
solutions to quizzes and in-class test will be provided, solutions to project and feedback on individual submissions will be provided