| Overall Educational Objective |
To gain a comprehensive understanding of cloud computing fundamentals, its associated technologies, and their integration with big data infrastructures, and to apply, design, implement, and optimize cloud-based solutions tailored for large-scale data applications. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Demonstrate a deep understanding of the fundamental concepts, advantages, and challenges associated with cloud computing;
2) Identify and explain core cloud-related technologies such as virtual servers, SAAS, IAAS, and cloud-based databases, understanding their operation and application scenarios;
3) Design and implement big data infrastructures like Hadoop and Spark within a cloud environment, optimizing for scalability and performance;
4) Design and implement strategies to integrate big data technologies seamlessly with cloud platforms, ensuring data integrity and efficient processing;
5) Apply best practices in the deployment of big data applications in cloud environments, focusing on optimization, cost-effectiveness, and performance metrics
6) Critically analyze real-world problems related to big data and cloud computing, proposing innovative and effective cloud-based solutions;
7) Understand and address the ethical and security implications of big data storage, processing, and analysis in cloud environments, ensuring data privacy and compliance with relevant regulations.
|
| Week |
Subject |
| 1) |
Introduction to Cloud Computing
Overview of cloud computing, history, and evolution.
|
| 2) |
Cloud Service Models: SAAS, PAAS, and IAAS
Deep dive into Software as a Service (SAAS), Platform as a Service (PAAS), and Infrastructure as a Service (IAAS).
|
| 3) |
Virtualization and Virtual Servers
The concept of virtualization, its benefits, types, and role in cloud environments.
|
| 4) |
Cloud-based Networks and Storage Solutions
Introduction to cloud networking, storage systems, and their relevance in cloud environments.
|
| 5) |
Big Data Fundamentals
What is big data? Characteristics, challenges, and importance.
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| 6) |
Big Data Infrastructures: Hadoop Ecosystem
Dive into the Hadoop ecosystem, its components, and their functionalities.
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| 7) |
Spark and Real-time Data Processing in Cloud
Introduction to Apache Spark and the advantages of real-time data processing in the cloud.
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| 8) |
Integrating Big Data with Cloud Platforms
Methods, tools, and strategies for integrating big data systems with cloud platforms.
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| 9) |
Optimization and Performance Tuning in the Cloud
Techniques to optimize performance for big data applications in the cloud.
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| 10) |
Security and Privacy in Cloud-based Big Data Systems
Challenges, solutions, and best practices to ensure data security and privacy.
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| 11) |
Cost Management and Scalability in Cloud Environments
Techniques and strategies for effective cost management and scalability in cloud infrastructures.
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| 12) |
Emerging Trends in Cloud Computing and Big Data
A look into the future: what’s next for cloud and big data technologies?
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| 13) |
Case Studies: Real-world Big Data Applications in the Cloud
Analyzing real-world examples of successful big data and cloud integration.
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| 14) |
Course Review and Future Implications
A recap of the entire course and a discussion on the future implications of cloud and big data technologies.
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| 15) |
Final Examintation Period |
| 16) |
Final Examintation Period |