INFO-H-419: Data Warehouses



  • Theory 24 h - Exercises 24h - Project 12h
  • 5 ECTS

Study Programme

  • Master in Computer Science and Engineering [MA-IRIF]
  • Master in Computer Sciences [INFO]
  • Erasmus Mundus Master in Big Data Management and Analytics (BDMA)


The course is given during the first semester

  • Lectures on Mondays from 10 am to 12 pm at the room S.C.3.122
  • Exercises on Tuesdays from 2 pm to 4 pm at the room S.UB4.136


  • Group project (30%)
  • Written exam (70%)
    • the exam is open book; notes and books can be used. Laptops and other electronic devices are not allowed. Please prepare your paper material in advance, not the day before the examination to avoid any printing problems.

Course Summary

Relational and object-oriented databases are mainly suited for operational settings in which there are many small transactions querying and writing to the database. Consistency of the database (in the presence of potentially conflicting transactions) is of utmost importance. Much different is the situation in analytical processing where historical data is analyzed and aggregated in many different ways. Such queries differ significantly from the typical transactional queries in the relational model:

  • Typically analytical queries touch a larger part of the database and last longer than the transactional queries;
  • Analytical queries involve aggregations (min, max, avg, …) over large subgroups of the data;
  • When analyzing data it is convenient to see it as multi-dimensional.

For these reasons, data to be analyzed is typically collected into a data warehouse with Online Analytical Processing support. Online here refers to the fact that the answers to the queries should not take too long to be computed. Collecting the data is often referred to as Extract-Transform-Load (ELT). The data in the data warehouse needs to be organized in a way to enable the analytical queries to be executed efficiently. For the relational model star and snowflake schemes are popular designs. Next to OLAP on top of a relational database (ROLAP), also native OLAP solutions based on multidimensional structures (MOLAP) exist. In order to further improve query answering efficiency, some query results can already be materialized in the database, and new indexing techniques have been developped.

In the course, the main concepts of multidimensional databases will be covered and illustrated using the SQL Server tools.


Extra books

The following materials have been used to construct the course material, but are not required reading for the course:


  • Database System Concepts (6th ed.) by Abraham Silberschatz, Henri Korth, and S. Sudarshan. McGraw-Hill, 2011.
    • ER-modeling: Chapter 7
    • Keys and functional dependencies: Section 8.3.1
    • BCNF: 8.3.2

Course Slides


All software used in the course is available in the computer labs. Students who wish a personal copy of the software on their own computers, can get free copies of the software. Succinct instructions to acquire the software have been included below; in case additional help is required you can contact the sysadmin of the department: Robin Choquet

  • MS SQL Server Tools: can be downloaded for free from Register on this page with your ULB email address, and 'order' the free msdnaa. After verification you receive login credentials to download quite a few software packages for free. Select the SQL Server 2014 Enterprise edition.


Group Project

TPC is a non-profit corporation that defines transaction processing and database benchmarks and disseminates objective, verifiable TPC performance data to the industry. Regarding data warehouses, two TPC benchmarks are relevant:

  • TPC-DS, the Decision Support Benchmark, which models the decision support functions of a retail product supplier.
  • TPC-DI, the Data Integration Support Benchmark, which models a typical ETL process that loads a data warehouse.

The project of the course consist of 2 parts:

  • Part I: Implement the TPC-DS benchmark (deadline 1/11/2023)
  • Part II: Implement the TPC-DI benchmark (deadline 24/12/2023)

You have free choice to use the tools on which the two benchmarks will be implemented. For example, the TPC-DS benchmark could be implemented on SQL Server Analysis Services, Pentaho Analysis Services (aka Mondrian), etc. Similarly, the TPC-DI benchmark could be implemented on SQL Server Integration Services, Pentaho Data Integration, Talend Data Studio, SQL scripts, etc., which then load the data warehouse on a DBMS such as SQL Server, Oracle, PostgreSQL, etc.

Furthermore, both benchmarks must be implemented with several scale factors, which determine the size of the resulting data warehouse. You DO NOT need to use the scale factors mentioned in the TPC requirements. The pedagogical objectives aimed at is that you learn how to properly perform a benchmark. Therefore, you need to estimate the biggest scale factor that you can put on your own computer: this will be your reference scale factor, say 1.0, and then you will need to have 3 smaller scale factors, e.g., at 0.1, 0.2, and 0.5 of the full size in order to see the evolution of the performance.

The project is carried out in groups of 3-4 persons, which will be the same for the two parts. Before you can submit part I of the project, you will have to register in a group. For this, please send an email to the lecturer with the information about your group by 1/10/2023 at the latest. The submission deadlines for parts I and II are strict.

The deliverables expected for each part of the project are the following:

  • A report in pdf explaining the essential aspects of your implementation, and
  • A zip file containing the code of your implementation, with all necessary instructions to be able to replicate your implementation by the lecturer in standard computing infrastructure.

The project evaluation will count for 30% of your total grade. This may seem undervalued, however, putting effort in the project will definitely help you in achieving a better understanding of the course material which will result in a better score in the paper exam which amounts for 70% of the grade.

Tools of the previous year

SQL Server, PostgreSQL, mySQL, Oracle, SQLite, mariadb, Spark SQL, DB2/Airflow, Microsoft Azure SQL, Citus, AWS Aurora, Google BigQuery, Impala

Groups of the current year

  • SQL Server: Enxhi Nushi, Gabriel Octavio Lozano Pinzón, Gian Carlo Tejada Gargate, José Carlos Lozano Dibildox
  • PostgreSQL: Dionisius Mayr, Jakub Kwiatkowski, Gabriela Kaczmarek, Arijit
  • Apache Hive: Yutao Chen, Qianyun Zhuang, Min Zhang, Ziyong Zhang
  • Spark SQL: Valerio Rocca, Alexandre Dubois, Arnaud Cools, Maria Camila Salazar
  • MySQL: Aryan Gupta, Dilbar Isakova, Hareem Raza, Muhammad Qasim Khan
  • DuckDB: Jintao Ma, Linhan Wang, Iyoha Peace Osamuyi, Hieu Nguyen
  • Oracle and Pentaho Data Integration: Sony Shrestha, Aayush Paudel, MD Kamrul Islam, Shofiyyah Nadhiroh
  • Amazon Redshift: Rana İşlek, Simon Coessens, Berat Furkan Koçak, David García Morillo
  • MariaDB: Izmar Soumaya, Ayadi Mustapha, Nils van Es Ostos, Narmina Mahmudova
  • SQLite: Benjamin Gold, François Diximier, Noah Laravine, Louai Bouzaher
  • DB2: Nicolas Lermusiaux, Gaetan Poupart-Lafarge, Ozan Basaran, Onur Bacaksiz

Examinations from Previous Years

teaching/infoh419.txt · Last modified: 2023/11/20 16:18 by ezimanyi