CLOUD DATA MANAGEMENT
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Delivery method
- Teaching methods
The course requires that students have good knowledge of relational data management systems.
Exams aim at verifying that students have successfully acquired the knowledge and abilities described in the "Objectives" section. The exam consists of a written test which lasts 2 hours. Students cannot consult any textbook or other learning material during the exam. The written exam consists of 2/3 open questions on the conceptual aspects of the course, and 3/4 exercises, which aim at assessing data modeling, or data analysis, or data distribution or data protection abilities learned by the students. The grade is given out of thirty. Students can do intermediate written tests to assess the acquired knowledge and abilities at mid and end of the course. The intermediate tests have the same structure as the final written exam. The grade of both tests is given in thirtieths. The exam is passed if a student receives a grade greater than 18/30 in both intermediate tests, or if he/she receives a grade greater than 18/30 in the final written test.
If a student has received a grade greater than 18/30 in both intermediate tests, the final exam score is the average value of the grades received in the intermediate tests. Otherwise, the final score corresponds to the grade of the written test.
The score of intermediate and final tests is derived as follows. Each question and exercise is associated with a weight corresponding to the maximum score the student can obtain by correctly carrying out the exercise. The weight can range from 3 to 8 points depending on the complexity of the related question or exercise. The final grade, specified out of thirty, is calculated as the weighted average of the correctness percentages of the related questions and exercises.
The course aims at providing the necessary knowledge and skills needed for the design and use of data management systems in cloud computing. After having attended the course, the student will be able to autonomously judge the services provided by cloud frameworks, to design new cloud-based solutions by taking into account current ICT standards, when available.
The course aims to
1- provide basic concepts related to cloud computing, cloud service models and cloud infrastructure deployment models.
2- train the student to the use of cloud computing enabling technologies.
3- initiate the student to the main paradigms for data analysis in the cloud
4- introduce data management systems for the cloud
5- analyze security and privacy issues of cloud data management.
The studied topics will be presented, where possible, by taking as reference the architectures of the principal commercial solutions (e.g., Microsoft, Amazon web service, etc.).
In addition, the course aims to develop in the student soft skills, such as the ability to autonomously learn new cloud data management technologies. It is expected that, after attending this course, the student will be able to assess the strengths and weaknesses of cloud data management technologies.
- Foundations of cloud computing (goal 1, Lectures 4h)
--The cloud model and its properties
--Service models for cloud computing
--Deployment models for cloud infrastructures
- Enabling technologies (goal 2, Lectures 10h -- 6h theory, 4h labs)
-- Introduction to virtualization
-- Containerization and orchestration
- Data management and analysis in the cloud (goal 3, Lectures 32h -- 12 h theory, 10 h exercise, 10h labs)
--Distributed computational paradigm
-Cloud-based NoSQL databases (goal 4, Lectures 10h -- 6 h theory, 2 h exercise, 2h labs)
--System families, data models and query languages
- Privacy and security issues (goal 5, Lectures 4h)
--Data protection techniques
See section Contents.
The course is composed of lessons (32 hours), exercise sessions (12 hours), and laboratory sessions (16h), as illustrated in the Course Content section of this syllabus.
Professors
Borrowers
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Degree course in: COMPUTER SCIENCE