| Overall Educational Objective |
This course aims to provide students with a thorough understanding of cloud computing fundamentals, the technologies involved, and how these can be integrated with big data infrastructures to create, design, implement, and optimize cloud-based solutions for large-scale data applications. The focus will be on leveraging cloud computing to address the needs of big data storage, processing, and analysis, ensuring scalability, reliability, and efficiency in data-driven environments. |
| Course Description |
ITC545 Cloud Computing explores the dynamic field of cloud computing and its critical role in supporting big data analytics and applications. Students will learn about the essential components of cloud computing, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and cloud-based databases. The course will cover the principles of cloud architecture, virtualization technologies, cloud security, and privacy concerns. It aims to equip students with the knowledge and skills to architect, deploy, and manage cloud solutions that effectively integrate with big data technologies, enhancing their capability to tackle complex data challenges. |
| Course Description in Turkish |
ITC545 Bulut Bilişim, bulut bilişimin dinamik alanını ve büyük veri analitikleri ve uygulamalarını desteklemedeki kritik rolünü incelemektedir. Öğrenciler, Altyapı Hizmeti Olarak (IaaS), Platform Hizmeti Olarak (PaaS), Yazılım Hizmeti Olarak (SaaS) ve bulut tabanlı veritabanları dahil olmak üzere bulut bilişimin temel bileşenleri hakkında bilgi edineceklerdir. Ders, bulut mimarisi prensipleri, sanallaştırma teknolojileri, bulut güvenliği ve gizlilik endişelerini kapsayacaktır. Büyük veri teknolojileriyle etkili bir şekilde entegre olan bulut çözümlerini tasarlama, dağıtma ve yönetme konusunda bilgi ve becerilerle öğrencileri donatmayı amaçlamaktadır, karmaşık veri zorluklarıyla başa çıkma yeteneklerini geliştirir. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Achieve a comprehensive understanding of cloud computing architectures, models, and services, recognizing their benefits and limitations in big data contexts.
2) Identify and explain the operation and application scenarios of core cloud technologies, including IaaS, PaaS, SaaS, and virtualization, and their roles in supporting big data analytics.
3) Design and deploy scalable big data infrastructures such as Hadoop and Spark within cloud environments, focusing on optimization for performance and cost-effectiveness.
4) Develop strategies for the seamless integration of big data technologies with cloud platforms, ensuring efficient data processing, integrity, and security.
5) Utilize best practices in deploying big data applications in the cloud, addressing considerations for scalability, reliability, and monitoring.
6) Critically analyze challenges at the intersection of cloud computing and big data, proposing innovative cloud-based solutions for data-intensive applications.
7) Understand the ethical and security implications of deploying big data solutions in cloud environments, ensuring compliance with data privacy laws and regulations
|
| |
Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to develop and deepen their knowledge in the field of
Information Technologies at the level of expertise based on their
undergraduate level qualifications. |
S |
Exam,HW
|
| 2) |
An ability to apply scientific and practical knowledge in statistics,
computing and computer science. |
H |
HW,Project
|
| 3) |
A Comprehensive knowledge of analysis and
modeling methods and their limitations. |
S |
Presentation
|
| 4) |
An ability to design and apply analytical, modeling and experimental H 2
based researches, analyzes and interprets complex situations
encountered in this process. |
N |
|
| 5) |
An ability to transmit the process and results of the work of
information systems systematically and clearly in written and oral form in
national and international environments. |
S |
Project
|
| 6) |
An understanding of data collection, processing, use,
interpretation and social, scientific and ethical values in all
professional and professional activities. |
N |
|
| 7) |
An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take
responsibility. |
N |
|
| 8) |
An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. |
N |
|
| 9) |
An ability to communicate verbally and in writing in English at least at
the level of B2 of CEFR. |
N |
|
| 10) |
An understanding the social and environmental aspects of IT
applications. |
N |
|
| Week |
Subject |
| 1) |
● Introduction to Cloud Computing
● Overview of cloud computing, history, evolution, and key concepts.
|
| 2) |
● Cloud Service Models: SAAS, PAAS, and IAAS
● Detailed exploration of Software as a Service (SAAS), Platform as a Service (PAAS), and Infrastructure as a Service (IAAS).
|
| 3) |
● Virtualization and Virtual Servers
● Understanding the concept of virtualization, its benefits, types, and its pivotal role in creating cloud environments.
|
| 4) |
● Cloud-based Networks and Storage Solutions
● Examination of cloud networking, storage systems, including object storage, block storage, and file storage, and their importance.
|
| 5) |
● Big Data Fundamentals
● Introduction to big data: definitions, characteristics, challenges, and the significance of big data in modern computing.
|
| 6) |
● Big Data Infrastructures: Hadoop Ecosystem
● In-depth look at the Hadoop ecosystem, including HDFS, MapReduce, YARN, and other components.
|
| 7) |
● Spark and Real-time Data Processing in the Cloud
● Overview of Apache Spark, its advantages over Hadoop for real-time data processing, and its role in cloud environments.
|
| 8) |
● Integrating Big Data with Cloud Platforms
● Strategies, tools, and methods for effectively integrating big data technologies with cloud platforms.
|
| 9) |
● Optimization and Performance Tuning in the Cloud
● Techniques for optimizing and tuning performance for cloud-based big data applications, focusing on resource management and scaling.
|
| 10) |
● Security and Privacy in Cloud-based Big Data Systems
● Addressing the challenges of security and privacy in cloud-based big data systems, including encryption, access control, and compliance.
|
| 11) |
● Cost Management and Scalability in Cloud Environments
● Discussing strategies for managing costs and achieving scalability in cloud infrastructures, including auto-scaling and resource allocation.
|
| 12) |
● Emerging Trends in Cloud Computing and Big Data
● Exploration of the latest trends and innovations in cloud computing and big data, including serverless computing, machine learning, and AI integration.
|
| 13) |
● Case Studies: Real-world Big Data Applications in the Cloud
● Analysis of real-world case studies showcasing successful integration of big data and cloud computing technologies.
|
| 14) |
● Course Review and Future Implications
● Recap of the course content, discussion on the future implications of cloud computing and big data technologies, and preparation for final assessments
|
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
Information Package / Course Catalogue