Checking date: 16/09/2020

Course: 2020/2021

Big Data
Study: Master in Computer Technologies Applied to the Financial Sector (313)


Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS


Competences and skills that will be acquired and learning results.
Basic and general competences - The students should apply their knowledge and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study - Students should communicate their conclusions and the knowledge and rationale, to specialists and non-specialists in a clear and unambiguous - Students must possess the learning skills that enable them to continue studying in a self-directed or autonomous way - Ability to understand and apply methods and techniques in the field of Computer Engineering in financial markets - Ability to conceive, design or create, implement and adopt a substantial process of developing and creating software for financial markets Specific skills - Analyze and evaluate the feasibility of implementing a data management system according to the needs - Analyze and understand the main tools for managing large amounts of data storage, access and review Learning outcomes are determined by both the contents of the subject as the framework main of this matter which is Systems decision support in the financial sector.
Description of contents: programme
Block I: Theoretical Foundation. ------------------------------------ Item 1: Introduction: Social and technological framework - The IT Society - Current role of information and data - Storage paradigms - Characterization of the Big Data concept Item 2: Approach to Big Data - Transactional vs. Analytical databases - Physical organizations suited to the process - Architectures: distributed systems and CAP. - ROLAP warehouses. Analytical operation in SQL. Item 3: Integration, transformation and Cleaning - Integration of sources - Transformation and Cleaning - Google Refine - SPARQL Block II: Implementing Big Data ------------------------------------ Item 4: BigData operability - The Map-Reduce paradigm - Legal and ethical aspects: Privacy and Security Item 5: Back-End for BigData I: MongoDB - Introduction to Mongo DB. - Basic operability in MongoBD - Aggregation in MongoBD. Pipeline and Map-Reduce. - Replication and Distribution in MongoBD Item 6: Back-End for BigData II: Cassandra - Cassandra's Basics - Design on Cassandra Item 7: Back-End for BigData III: Hadoop - The HADOOP ecosystem and its installation - SandBox - HADOOP functionality - Map-Reduce in HADOOP
Learning activities and methodology
Theory class: presentation with digital support of the basic content of the course. Hours: 22 Worklabs + problems: Application of theory to several big data software. Hours: 28 + 18 Tutorial: both face to face and video-conference E-learning activities: participation in the activities that the teacher proposes.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Basic Bibliography
  • Apache¿ Hadoop®. Apache¿ Hadoop®. 2016
  • MongoBD. MongoBD. 2016
Recursos electrónicosElectronic Resources *
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus and the academic weekly planning may change due academic events or other reasons.