This course introduces the concepts of database technology used in Business Intelligence. More precisely, this includes multidimensional databases and data warehouses, as well as ETL (Extraction, Transformation and Load) processes and basic concepts of dashboarding. Necessary techniques will be presented for designing, implementing, exploiting, and maintaining data warehouses, paying special attention to spatio-temporal data.
A particular focus will be given on the problems posed by heterogeneous data integration and data quality. The students will learn how to define, measure and maintain data quality in the context of data warehousing. Classical notions of data warehousing and OLAP are developed: ETL, architecture, conceptual and logical design, query processing and optimization. At the end of this course, the student will know how to efficiently design, construct and query a data warehouse to create effective visualizations.
Person in charge
Alberto Abello Gamazo (
Petar Jovanovic (
CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.
CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.
Entrepreneurship and innovation
CT1 - Know and understand the organization of a company and the sciences that govern its activity; have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.
CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
Generic Technical Competences
CG1 - Identify and apply the most appropriate data management methods and processes to manage the data life cycle, considering both structured and unstructured data
CE2 - Apply the fundamentals of data management and processing to a data science problem
CE3 - Apply data integration methods to solve data science problems in heterogeneous data environments
CE5 - Model, design, and implement complex data systems, including data visualization
CE7 - Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
Database physical desing for analytical queries
Star-join and join indexes; Bitmaps; Materialized views; Spatio-temporal data
Extraction, Transformation and Load
Data quality; Schema and Data Integration; ETL management
Visualization and descriptive analytics
Key Performance Indicators; Dashboarding
In these activities, the lecturer will introduce the main theoretical concepts of the subject. Besides lecturing, cooperative learning techniques will be used. These demand the active participation of the students, and consequently will be evaluated. Objectives:132 Contents:
Theory: Inverted class techniques will be used, which require that the student work on the provided multimedia materials before the class. Then, theory lectures comprise the teacher's complementary explanations and problem solving.
Laboratory: Some representative tools will be used for the application of theoretical concepts (e.g., Indyco Builder, PotgreSQL, Oracle, Pentaho Data Integration, Tableau). The course includes continuous hands-on through a course project, divided into three logical blocks: data warehouse modelling, data integration and migration (ETL), and descriptive visualisation, in which the students will work in teams. There will be three project deliverables outside the class hours, while in the class the students will be as well individually assessed about the knowledge acquired during each project block.
Specifically, it will be assumed knowledge on:
- UML class diagrams
- Relational algebra
- SQL queries
- Relational views
- B-tree operations (i.e., insertion and splits)
- Basic concepts on physical query optimization
El contingut de l'assignatura no canviarà.
The content of the subject will not change.
Les explicacions complementàries del professor, resolució de problemes i tutories de projecte es faran de forma remota.
Teacher's complementary explanations, problem solving, and project tutoring will be done remotely.
El métode d'avaluació no canviarà.
Evaluation method will not change.
La realització d'exercicis, discussions i resolució de dubtes es farà de forma remota.
Exercises, discussions and doubt resolution will be moved to remote.
Where we are
B6 Building Campus Nord
C/Jordi Girona Salgado,1-3
08034 BARCELONA Spain
Tel: (+34) 93 401 70 00