This module focuses on Distributed Data Processing. The module will cover parallel processing

The following will have an emphasis on distribution data processing

Business Case Evaluation
Data Identification (external and internal)
Data Acquisition & Filtering
Data Extraction
Data Validation & Cleansing
Data Aggregation & Representation
Data Analysis
Data Visualization
Utilisation of Analysis Results (alerts, application, business process optimisation)

Other related areas include:

Clusters File Systems & Distributed File Systems
NoSQL
Distributed Data Processing
Parallel Data Processing
Processing Workloads (batch and transactional)
Cloud Computing

Practical element weighted at 50%. Covers learning outcome 1.

Report weighted at 50%: 1500 word Management style report, based on a scenario, which identifies the issues involved with the realms of distributed data processing. This covers learning outcomes 2 and 3.

The VLE will provide supporting learning materials. The module will use a discussion forum and blog posts to allow students to share ideas and expertise. Tutor support will be available via the discussion forum and Skype and also by email and telephone.

MS Office
Internet
NoSQL Datastores (MongoDB, CouchDB)
Hadoop Framework

(note that all the text listed here are available through the university e-books service. It is not expected that students will buy these texts. The nature of the subject means that these texts will be constantly updated.)

Achari, 2015 Hadoop Essential Packt Publishing

1) Design, build and critically evaluate a proof of concept that would solve a complex issues for organisation with respect to distributed data processing issue. (Application, Analysis, Problem Solving).
2) Critically discuss distributed processes with respect to a distributed data processing issue. (Knowledge and Understanding, Enquiry).
3) Critically discuss other options that would solve the issues raised in the case study and present your findings. (Communication).