Checking date: 22/05/2024

Course: 2024/2025

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)
- Understand the concept and all dimensions of Big Data technology. - Explore the social, business, and technological contexts underlying the emergence and expansion of this technology. - Comprehend the Information Life Cycle and the processes that sustain it. - Analyze the needs prompted by information: acquisition, transformation, storage, and exploitation. - Study the related technologies and the components of a Big Data system (front-end and back-end systems). - Become familiar with the characteristics and use of various tools supporting Big Data. -- Differentiate between structured mass storage and NoSQL Management Systems. -- Get introduced to NoSQL management through a Document-Oriented System (MongoDB). Learn how to manipulate data with this tool and study practical techniques for replication and distribution of data collections to implement massively parallel systems. -- Get introduced to other types of NoSQL management: column-oriented systems (Cassandra) and graph-oriented systems (Neo4J). -- Introduction to the Hadoop suite of tools.
Skills and learning outcomes
Description of contents: programme
Block I: Theoretical Foundation. ------------------------------------ Item 1: Introduction: Social and technological framework - Role of Information in the IT society - Need and types for Data Systems - Characterization of the Big Data concept - Implementation of Big Data - Legal and ethical aspects Item 2: Storage and No-SQL Technologies - Storage technologies: structures and processes - Transactional DB vs. Analytical DB - Architectures. Distributed Systems and CAP. - Distributed operability: MapReduce paradigm - Classification of NoSQL systems Item 3: Integration, transformation and Cleaning - Integration of sources - Transformation and Cleaning - Google Refine - SPARQL Block II: Tools Supporting Big Data: Main commercial tools for Storage, Report, and Visualization ------------------------------------ Item 4: Back-End for BigData I: MongoDB - Basic Operation in MongoBD - Aggregation in MongoBD. Pipeline and Map-Reduce. - Replication and Distribution in MongoBD Topic 5: Back-End for BigData II: Neo4J - Introduction to linked Data: Graphs - Graph based DB models. Languages. - Property Graph DB: Neo4J Item 6: Back-End for BigData III: Cassandra - Cassandra's Basics - Design on Cassandra Item 7: Back-End for BigData IV: Hadoop - The HADOOP ecosystem and its installation - SandBox - HADOOP functionality - Map-Reduce in HADOOP
Learning activities and methodology
Learning activities: AF1: Theoretical classes: presentations accompanied by digital supporting materials. AF3: Theoretical practical classes: Combination of theoretical classes accompanied by the resolution of practical exercises. AF4: Laboratory practices: Practices to be developed in specific laboratories for the different subjects. AF5: Tutorials: Face-to-face and / or distance tutorials (videoconference). AF2: E-learning activities: tutorials, recommended reading, documentation. AF7: 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. Teaching methodologies MD1: Lectures 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. MD2: 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. MD3: Resolution of practical cases, problems, etc. raised by the teacher MD4: 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 MD5: Preparation of work and reports individually or in groups MD6: 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 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40

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.