Cloud Computing with Big Data

About the Course

This is a project-based workshop that exposes students to the theories and techniques of cloud computing and the use of cloud-native open-source systems to build big data analytics applications.

Learning Objectives include:

understanding of key principles of cloud computing concepts, models, technologies and its application for big data.

The workshop is divided into two parts: a 3-hr lecture that introduces basic cloud computing concepts, modules and technologies, and a project to develop big data cloud applications augmented with five 2-hr project-related lectures. 

I. Topics include but not limited to: 

  • Concepts and principles of cloud computing
  • Cloud computing service (delivery) models
  • Virtualization technology
  • Container orchestration framework
  • Cloud native ecosystem
  • Big data frameworks
  • Messaging and data services
  • Stream processing engines

II. Cloud-based Big Data Projects - The learning outcome of the team-project is to design a big data application and to develop its implementation on a public cloud. A hackathon-like approach will be adopted to allow students to suggest ideas and form teams based on individual interests and skills. Four 2-hr lectures cover programming PaaS and SaaS IBM cloud services and pattern-based approach to design and implement big data applications. Students learn by examples with hands-on laboratories. For data, students can tap on the rich Singapore Smart Nation Open Government Data repositories among others. 

Prerequisite: knowledge of programming is compulsory; proficiency in JAVA and/or GO will be helpful; background in Linux operating system will be useful.

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About the Lecturer

Richard T. B. Ma

Department of Computer Science, School of Computing, NUS

Richard T. B. Ma is currently an Associate Professor with the School of Computing, National University of Singapore. He received the B.Sc. (Hons.) degree in computer science and M.Phil. degree in computer science and engineering from The Chinese University of Hong Kong in 2002 and 2004, respectively, and the Ph.D. degree in electrical engineering from Columbia University in 2010. During his Ph.D. study, he worked as a Research Intern at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, and Telefonica Research, Barcelona, Spain. From 2010–2014, he worked as a Research Scientist at the Advanced Digital Science Center (ADSC), University of Illinois at Urbana–Champaign, Champaign, IL, USA. His current research interests include distributed systems and network economics. He was a recipient of the Best Paper Award Runners-up from the ACM Mobihoc 2020 and a co-recipient of the Best Paper Award from the IEEE IC2E 2013, the IEEE ICNP 2014, and the IEEE Workshop on Smart Data Pricing 2015. He is a Senior Member of ACM and IEEE.

Past Projects