Cloud Computing and Big Data Analytics

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 "Erasmus Mundus Master in Information Technology for Business Intelligence ".

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

Lecturer

Angel Toribio-González  ()

Tentative calendar

Class topics and laboratory sessions
Tuesday (Group 11. Laboratory) Thursday (Lectures) Friday (Group 12. Laboratory)
February 20 Environment configuration and warm up 22 Presentation of the subject 23 Environment configuration and warm up
27 Basic knowledge toolbox 1 Introduction to Cloud Computing 2 Basic knowledge toolbox
March 6 Doors in the cloud 8 Virtualization 9 Doors in the cloud
13 Extracting and analyzing data from the cloud 15 Best practices for creating SaaS
The twelve factor methodology
16 Extracting and analyzing data from the cloud
20 Creating a web application using cloud PaaS 22 Service oriented architectures 23 Creating a web application using cloud PaaS
27 Easter 29 Easter 1 Easter
April 3 Enhancing your web app using additional cloud services 5 Basic concepts on cloud computing architecture 6 Enhancing your web app using additional cloud services
10 Interacting with users and services in the Cloud 12 Usage of cloud components to follow a set of best practices on cloud architecture
Amazon Web Services use case
13 Interacting with users and services in the Cloud
17 Interacting with users and services in the Cloud
Optional part
19 Making the architecture components work together: Examples of usage 20 Interacting with users and services in the Cloud
Optional part
24 Using the Elastic Stack to study scraped data from a web page
Student project kick off
26 Agile framework for DevOps
Guidelines to develop the student coding project
27 Using the Elastic Stack to study scraped data from a web page
Student project kick off
May 1 May 1st 3 Change schedule - Monday 4 FIB's day
8 Student project development 10 8-9: Student research project Pecha Kutcha presentations

9-10: TBA
11 Student project development
15 Student project development 17 8-9: Student research project Pecha Kutcha presentations

9-10: Keynote presentation by New Relic on monitoring cloud infrastructures
18 Student project development
22 Student project development 24 8-9: Student research project Pecha Kutcha presentations

9-10: Student research project Pecha Kutcha presentations
25 Student project development
29 Student project development 31 8-10: Keynote presentation by Amazon Web Services on their offering for Machine Learning and Artifical Intelligence 1 Student project development
June 5 Student project technical interview 7 Student project Pecha Kutcha presentations 8 Student project technical interview

Evaluation

Laboratory (2 persons) 30%
Lab 0 Environment configuration and warm up 0.0%  
Lab 1 Basic knowledge toolbox to get started in the Cloud 5.0%  
Lab 2 Doors in the cloud 5.0%  
Lab 3 Extracting and analyzing data from the cloud 5.0%  
Lab 4 Creating a web application using cloud PaaS 5.0%  
Lab 5 Enhancing your web app using additional cloud services 5.0%  
Lab 6 Interacting with users and services in the Cloud 5.0%  
Homework (2 persons) 20%
Lab 5 (optional part) Enhancing your web app using additional cloud services 2.5%  
Lab 6 (optional part) Interacting with users and services in the Cloud 5.0%  
Lab 7 Using the Elastic Stack to study scraped data from a web page 7.5%  
Lab 8 Topic to read, research and present 5.0%  
Project (4 persons) 25%
Participation (individual) 15%
Attendance (individual) 10%