Big Data and Cloud Computing

Code:

CC4053

Acronym:

CC4053

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2018/2019 - 2S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~edrdo/aulas/bdcc
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master's Degree in Network and Information Systems Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:DS	29	Official Study Plan since 2018_M:DS	1	-	6	42	162
MI:ERS	41	Plano Oficial desde ano letivo 2014	4	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

Introduction to the use of cloud computing infrastructures for processing massive amounts of data ("big data") in real-world problems.

Learning outcomes and competences

- Use of cloud computing services  for big data applications.
- Programming big data applications using cloud programming models.
- Understanding of core fundaments and algorithms for mining big data.
- Hands-on practice with state-of-the-art tools for cloud computing and big data.

Working method

Presencial

Program

- Introduction to big data processing: challenges, example problems from science and business.

- The cloud computing paradigm: service models (PaaS, SaaS, IaaS); service virtualization, deployment and orchestration; integration of computing, networking and storage resources; scalability, fault-tolerance, and “elasticity”.

- Cloud storage solutions for big data: cloud file systems, NoSQL and graph-based databases, “object stores”.

- High-performance big data applications using cloud programming models: MapReduce, stream-based programming.

- Programming assignments on big data applications on specific topics such as data streams, social-network graphs, recommendation systems, or bioinformatics.

Mandatory literature

Dan C. Marinescu; Cloud Computing - Theory and Practice, 2nd edition, Morgan Kaufmann, 2018. ISBN: 978-0-12-812810-7
Jure Leskovec, Anand Rajaraman, Jeff Ullman ; Mining of Massive Datasets, Cambridge University Press, 2014. ISBN: 978-1107077232 (Available free in PDF format by the authors at http://mmds.org)
M. Zaharia and B. Chambers; Spark: The Definitive Guide - Big Data Processing Made Simple, O'Reilly, 2018. ISBN: 978-1491912218

Complementary Bibliography

Tom White; Hadoop, The Definitive Guide, 4th edition, O'Reilly Media, 2015. ISBN: 978-1491901632
N. Marz and J. Warren; Big Data: Principles and best practices of scalable realtime data systems,, Manning Publications, 2015. ISBN: 978-1617290343

Teaching methods and learning activities

- Introduction of cloud computing technologies in tandem with big data application requirements.

- Hands-on practice in programming projects using tools by major cloud service providers (Amazon Web Services, Microsoft Azure, Google Cloud, etc) and DCC computer clusters for MapReduce.

 

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	60,00
Trabalho prático ou de projeto	40,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	52,00
Frequência das aulas	52,00
Total:	104,00

Eligibility for exams

--

Calculation formula of final grade

- Final exam (60%)
- Programming projects (40%).