Data Warehousing

7.5 Credits
Second Cycle
School of Information and Engineering
Subject field
Information Systems (IKA)
Group of Subjects
Informatics/Computer and Systems Sciences
Disciplinary Domain
Technology, 100%
This course can be included in the following main field(s) of study
Microdata Analysis1
Progression indicator within (each) main field of study
Approved, 29 April 2019.
This syllabus is valid from 22 June 2019.

Learning Outcomes

Knowledge and understanding

After completing the course the student shall be able to:
  • Describe central concepts in the field of relational databases and data warehousing
  • Describe the architecture and components in a data warehouse
  • Explain the significance of normalisation for a design solution of a relational database.
  • Explain the importance of designing good tables identifiers in relational databases.

Skills and abilities

After completing the course the student shall be able to:

  • Apply basic principles of relational databases in accordance with the relational model.
  • Manipulate data and create, modify and delete database objects.
  • Use concept modeling and graphical description techniques to design relational databases corresponding to at least the third normal form.
  • Model data according to the principles of the Kimball and Immon methods
  • Use ETL (Extract, Transform, Load) processes to extract data from, change data in and add data to a data warehouse
  • Design and implement a simpler business intelligence solution, based on data warehouse architecture evaluation

Judgement and approach

After completing the course the student shall be able to:

  • Choose the appropriate method for data modeling to solve a specific business problem

Course Content

The course introduces the relational database concept and system theory, and design methods for developing database systems. The reason for using databases in companies and organisations is also covered.

The course deals with key concepts in the field of Data Warehousing (DW) and the general architecture of data warehouses. Furthermore, various types of data models are treated, e.g. Dimensional Data Store according to the Kimboll Model, and Normalized Data Store according to the Immon Model. The course also addresses terminology as ETL processes, OLAP (Online Analytical Processing), Data Mart, Data Lake, operational databases and common data sources.


Written examination 4.5 credits and written presentation of laboratory work 3 credits.

Forms of Study

Lectures and laboratory sessions.


The Swedish grades U–VG.

Written presentation of laboratory work U-G.
Written examination U-VG.


  • Bachelor’s degree in Statistics, Economics, Business Administration, Computer Science, Information Science or Informatics comprising at least 180 credits and English 6
  • Fundamentals of programming 7,5 credits

Other Information

The number of examinations per module is maximized to five.