| 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 Description in Turkish |
Bulut Bilişimde Büyük Veri dersi, bulut bilişim ve büyük veri teknolojilerinin kesişimine odaklanmaktadır. Öğrenciler, sanal sunucular, SAAS, IAAS ve bulut tabanlı veritabanları gibi bulutla ilgili teknolojilere ve bunların büyük veri altyapılarıyla nasıl entegre edildiğine dair bilgi edineceklerdir. Ders, öğrencilere büyük ölçekli veri zorlukları için optimize edilmiş bulut çözümlerini tasarlama ve uygulama becerilerini kazandırmayı amaçlamaktadır. |
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.
|
| 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 |