| CODE | CIS5230 | ||||||||
| TITLE | Data Analytics on the Cloud | ||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||
| MQF LEVEL | 7 | ||||||||
| ECTS CREDITS | 5 | ||||||||
| DEPARTMENT | Computer 福利在线免费 Systems | ||||||||
| DESCRIPTION | This study-unit covers the paradigms and tools used to analyze data on the cloud. It offers a practical perspective on setting up the cloud environment, using one of the three main cloud providers (i.e. Amazon Web 福利在线免费, Google Cloud, Microsoft Azure). The students will be exposed to the Map Reduce paradigm. They will gain practical experience in using cloud, data storage, and Big Data technologies such as Docker, Apache Hadoop, NoSQL systems (such as MongoDB, Cassandra and Neo4j). Stream processing will also be covered. The business implications of using these systems will be detailed. Study-Unit Aims: This study-unit is targeted towards the Data Analytics industry professional who needs to expand own knowledge base to include Cloud based technologies. Many Analytics features and functionalities are readily available with well known Cloud Service Providers such as Microsoft Azure and Amazon Web 福利在线免费. This study-unit will allow the student to explore technologies such as these to enable them to participate not only in using these technologies directly but understanding the business implications of CAPEX v OPEX and when to take advantage of such offerings. Other industry essential topics relevant to Cloud technology utilisation will be covered such as security, scalability and performance. This skillset will prove essential in the coming years in this field. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain what Cloud Computing is and the common terminology; - Explain the nature of Analytics and how they relate to Cloud Computing; - Explain the provisioning of Data Analytics in the context of Cloud Computing; - Explain commercial options such as Data Analytics as a Service (DAaaS); - Explain performance and data security issues on the Cloud regarding DAssS; - Explain the sharing, storage and migration of Enterprise data on the Cloud; - Familiarity with Models/Techniques for Scalable Cloud-Based data Analytics; - Apply the Map-Reduce framework for Data Analytics; - Use of NoSQL systems for the storage of data in the cloud; - Explain the use-cases for cloud stream processing systems; - Demonstrate understanding the difference between CAPEX and OPEX and how to leverage savings from utilisation of Cloud services; - Knowledge of Azure and AWS and how they can be applied in a Data Analytics context. 2. Skills: At the end of this study-unit the student should have the skillset to: - Be able to identify candidate analytics tasks that can utilise Cloud technologies; - Design a cloud system for the analysis of Big Data on the cloud; - Program an analytics system using the Map-Reduce Paradigm; - Be able to formulate, implement and execute Analytics tasks on industry standard technological platforms such as Microsoft Azure and Amazon Web 福利在线免费 (AWS); - Be aware of the CAPEX and OPEX tradeoffs that are present when making business decisions regarding whether or not to utilise Cloud technologies or keep things "in house鈥�; - Be able to recognise when security, performance or scalability issues are present and address them utilising Cloud technologies. Main Text/s and any supplementary readings: - Williams, B. The Economics of Cloud Computing. 2012. Cisco Press. ISBN13: 978-1-58714-306-9. - Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman. Cambridge University Press, 2014. - Hadoop in Practice, Alex Holmes (Manning 2012). |
||||||||
| ADDITIONAL NOTES | Pre-requisite Qualifications: Proficiency in computer programming. | ||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||
| METHOD OF ASSESSMENT |
|
||||||||
| LECTURER/S | |||||||||
|
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
|||||||||