Checking date: 20/05/2022

Course: 2022/2023

Big Data
Master in Financial Sector Technologies: FinTech (Plan: 461 - Estudio: 313)


Department assigned to the subject: Computer Science and Engineering Department

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
- Structured Databases - Algebraic Data Languages - SQL - OLAP Databases and Data Warehouse Programming skills (desirable basics of Javascript)
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.
Skills and learning outcomes
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
Theoretical classes: presentations accompanied by digital supporting materials. Theoretical practical classes: Combination of theoretical classes accompanied by the resolution of practical exercises. Laboratory practices: Practices to be developed in specific laboratories for the different subjects. - Total number of hours: 23 Face-to-face: 100% Tutorials: Face-to-face and / or distance tutorials (videoconference): Total number of hours: 7 Face-to-face: 100% E-learning activities: tutorials, recommended reading, documentation: Total number of hours: 50 Face-to-face: 0% Individual student work: Individual student activities that complement the rest of the activities (both face-to-face and non-face-to-face), as well as exam preparation: No. Total hours: 70 Face-to-face: 0% Teaching methodologies -Exhibitions in class by the teacher with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. -Critical reading of texts recommended by the professor of the subject: press articles, reports, manuals and / or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. -Resolution of practical cases, problems, etc. raised by the teacher -Exhibition and discussion in class, under the moderation of the teacher of topics related to the content of the subject, as well as practical cases -Preparation of work and reports individually or in groups -Specific e-learning activities, related to the semi-face-to-face nature of the degree, self-correction activities, participation in forums, and any other online teaching mechanism
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment
Basic Bibliography
  • Apache¿ Hadoop®. Apache¿ Hadoop®. 2016
  • MongoBD. MongoBD. 2016
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.