COMP30770

Learning Outcomes:

On successful completion of this module the learner will be able to:
- Review the data processing using Shell and traditional data management systems using SQL;
- 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 (e.g., using HDFS) that support big data programming
- Understand Big Data programming models such as Map/Reduce and Spark, and how to use them on real examples
- Understand other Spark extensions for various big data applications such as MLlib, GraphX, Spark Streaming, etc.

Student Effort Hours:

Approaches to Teaching and Learning:

Requirements, Exclusions and Recommendations

To succeed in this module, students should normally have passed a programming module (e.g., COMP20250 Introduction to Java), a system module (e.g., COMP20180 Intro to Operating Systems) and a database module (e.g., COMP20070 Databases and Information Systems). If a student has not successfully completed these modules, then they should consult the Module Coordinator before registering.

Module Requisites and Incompatibles

Assessment Strategy

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Group/class feedback, post-assessment
• Self-assessment activities

How will my Feedback be Delivered?

solutions to lab practices will be provided;