Checking date: 30/08/2022

Course: 2023/2024

Technological fundamentals in the Big Data world
Master in Big Data Analytics (Plan: 352 - Estudio: 322)

Coordinating teacher: CARRETERO PEREZ, JESUS

Department assigned to the subject: Computer Science and Engineering Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Basic Skills Knowledge and understanding that provide a basis or opportunity for originality in developing and / or applying ideas, often in a research context Students must possess the learning skills that enable them to continue studying in a way that will be largely self-directed or autonomous. General Competencies Apply the fundamentals of software and hardware to meet the necessary technologies for the collection, storage, processing and reporting, especially for large volumes of data as a basis for the development and adaptation of such techniques to specific problems Specific Skills Apply knowledge of programming which to base the teaching of advanced technologies and methods for treating large volumes of data. Know the main technologies used to build big data systems, and the main characteristics of them. Use the basic results of work stoppage and distribution of advanced methods as a basis for prediction and classification Learning Results -Use Of programming techniques in the design and analysis of data processing methods - Ability to apply advanced technology to data processing - Knowledge and use of basic concepts of parallel and distributed programming, algorithm design and storage systems for the development of data processing applications
Skills and learning outcomes
Description of contents: programme
1 Introduction 2 Computing platforms for Big Data * Computing architectures * Parallel computing * Advanced computing platforms * Big Data frameworks 3 Distributed Memory Systems: * Clusters * Clouds * Data centers 4 Storage systems for big data * Distributed file systems * Parallel file systems * Storage technologies 5 Virtualization techniques * Virtualization fundamentals * Virtualization techniques * Server consolidation techniques 6 Parallel and distributed computing paradigms * Parallel Python * Web services * Pyspark * Fault tolerance 7 Use cases: * Data analytics * Data streaming
Learning activities and methodology
Learning activities: * Lectures * Hands-on land lab projects * Personal student work. Teaching methodology: * Presential lectures imparted in the class, using multimedia and informatics support, to develop the main concepts of the course. Reading materials will be provided to complement students knowledge. * Reading of recommended texts, from papers, technical journals, manuals and reports, to extend the student knowledge of the subject topics. * Solving practical jobs, problems, etc. proposed in class (individually or in groups).
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment

Basic Bibliography
  • Kai Wan and Min Chen. Big-Data Analytics for Cloud, IoT and Cognitive Computing. Wiley. ISBN-13: 978-1119247029
  • Vivek Kale. Parallel Computing Architectures and APIs: IoT Big Data Stream Processing 1st Edición. Amazon, Edición Kindle. 2019

The course syllabus may change due academic events or other reasons.