Cloud Computing and Big Data Analytics
Spring 2024

Cloud computing is a service model for large-scale distributed computing based on concentrated infrastructure and a set of collaborative services over which applications can be deployed and run over the network.

This course about Cloud computing has a mainly practical approach dealing with the related technologies. While most computer applications can be deployed in the cloud using the concepts explained, the classes pay particular attention to the creation of Big Data Analytics applications on the Cloud. It is offered to students of both degrees: " Master in Innovation and Research in Informatics " and " Master in Data Science ".

In the lectures of this course, the students will learn the principles and the state of the art of large-scale distributed computing in a service-based model. Students will study how scale affects system properties, models, architecture, and requirements.

In the laboratory sessions of this course, the students will gain a practical view of the latest in Cloud technology to implement a prototype that meets a business idea created by the student.

Class Contents

• Syllabus.
• QuickStart: Hands-on experience for the students of the master's course enrolled on this subject.
• Laboratory assignments.
• Course Project.

Lecturer

Angel Toribio-González  ( )

Tentative calendar!!!

	Class topics and laboratory sessions
	Monday (Lectures)		Wednesday (Laboratory)
February	12	Presentation of the subject	14	Environment configuration and warm up
	19	Introduction to Cloud Computing	21	Basic knowledge toolbox
	26	Virtualization	28	Doors in the cloud
March	4	Cloud computing architecture	6	Basic use of the cloud (1/2)
	11	Cloud computing architecture	13	Basic use of the cloud (2/2)
	18	Cloud computing architecture	20	Deploy a custom web app using additional cloud services
	25	Easter break	27	Easter break
April	1	Easter break	3	Mid-term exams
	8	Mid-term exams	10	Using the Elastic Stack to study scraped data from a web page
	15	Best practices for creating SaaS The twelve factor methodology Service oriented architectures	17	Advanced Analytics as a Service in the Cloud
	22	Cloud security	24	Programming your cloud infrastructure
	29	Cloud security	1	Labor day
May	6	Student project development	8	Student project development
	13	Research topics presentation	15	Student project development
	21	Tuesday Student project development	22	Student project development
	27	Student project Final presentation presence required	29	Student project Technical interview 8:00 - 12:00 (25 min pre-scheduled slot) presence required to pre-scheduled interviews
June			19	Final Exam 08:00 - 11:00

Evaluation