Computation Technologies for Big Data

The every time greater quantity of accessible information through Internet does that the efficient processing of big quantities of data was every time of greater interest. This has carried to the development of new techniques of storage and processing of large quantities of information, techniques that adapt of natural form to the systems distributed.

The main aim of this matter is to give to know different techniques of processing of big amounts of information, instructing to the student in their utilisation for the processing of the so named Big Data.

1. Big Data and MapReduce
2. Introducing Hadoop
3. HDFS
4. MapReduce with Hadoop
5. Apache Spark
6. Other technologies: Apache Flink

Basic bibliography
- Classnotes provided by the teacher
- T. White, Hadoop: The Definitive Guide, 4th Edition, O'Reilly, 2015
- B. Chambers, M. Zaharia, Spark: The Definitive Guide, O'Reilly, 2018

Complementary bibliography
- Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, Learning Spark. Lightning-Fast Big Data Analysis, O'Reilly, 2015
- Chuck Lam, Hadoop in Action, Manning, 2011
- Fabian Hueske, Vasiliki Kalavri, Stream Processing with Apache Flink", O'Reilly, 2019

-Students will be able to install, configure and manage the basic software for processing massive data.

- Students will be able to implement specialized codes in different languages in the massive data processing.

- The student will know and learn to use some of the tools available to develop and run applications for massive data cloud.

- The student will acquire the necessary skills for search, selection and management of resources (literature, software, etc.) related to Big Data.

Degree competences that work (see memory title):
- Basic: CB6, CB10.
- Transverse / General: T1, G2, G5, T4.
- Specific: E3, E4, E5.

-Lectures, in which the content of each subject is discussed. The student will have copies of the slides in advance and the teacher will promote an active attitude, asking questions that may clarify specific aspects and leaving open issues for student reflection.

- Practical classes in the computer classroom, allowing students to become familiar from a practical standpoint with the issues discussed in the lectures.

EDUCATIONAL ACTIVITIES character and its relationship with the competences of the degree

- Lectures: given by the teacher and presentation of seminars. Worked competencies: CB6, E3, E4, E5.
- Practical classes of laboratory, problem solving and case studies. Worked competencies: CB10, T1, T4, G2.
- Scheduled tutorials: guidance for the conduct of individual or group work, solving doubts and ongoing evaluation activities. Worked competencies: T1.
- Consideration. Worked competencies: CB6, T1, G5, E3, E4, E5

Training activities not attending classes and their relationship to the competences of the degree:

- Personal work: consulting literature, self-study, development of programmed activities, preparing presentations and papers. Number of hours: 76.5. Worked competencies: CB10, T1, T4, G2, G5

Ordinary opportunity:

Contribution to final mark and assessment criteria:

- Laboratory practicals: 90% of the mark
Students will tackle the resolution of various problems proposed in the computer classroom. They will have to carry out assignments in which the results obtained will be presented. Some of these works will be compulsory and others optional, which will allow them to get a higher mark. All assignments must be handed in before the dates specified and must meet the minimum quality requirements to be taken into consideration. The degree of compliance with the specifications, the methodology and thoroughness and the presentation of results will be assessed. In this part the competences CB6, G5, E3, E4, E5, CB10, T1, T4, G2 will be implicitly or explicitly assessed.

- Continuous and objective monitoring of active participation: 10%. Competence assessed T1

In order to pass the subject, a total score of 5 or higher must be achieved, being compulsory to have handed in all the practices indicated as compulsory.


Students who are not new enrollment do not keep grades from previous years. In order to pass the course, it is essential to have delivered all the practices indicated as compulsory.

Recovery opportunity (July) and extraordinary:
The evaluation will be the same as in the ordinary opportunity. Students who did not submit the proposed works throughout the semester must submit them before the established date.

Condition for Not Submitted qualification: no practice submitted.

In the case of fraudulent performance of exercises or tests, the regulations of the Normativa de avaliación do rendemento académico dos estudantes e de revisión de cualificacións will be applied.
In the application of the Normativa da ETSE sobre plaxio (approved by the ETSE Council on 12/19/2019), the total or partial copy of any practical ot theory exercise will mean failure on both opportunities of the course, with a grade of 0.0 in both cases.

-Blackboard classes: 12 contact hours + 20 h autonomous work of the student
- Practical classes: 18 contact hours + 46.5 h autonomous work of the student
- Tutorials and evaluation activities: 6 contact hours + 10 h autonomous work of the student

- Total: 112.5 h

Due to the strong interrelation between the theoretical and practical parts and the progressive presentation of closely related concepts in the theoretical part, it is advisable to dedicate a daily study or review time.

- Classes are taught in Spanish. Videoconferencing, chat, etc.: intensive use of online communication tools will be made
The software tools used in this subject are open source.