The students are able to explain the architecture of Business Intelligence architecture.
Students are able to represent the ETL process and describe the integration in data warehouse.
They can perform analyses and visualizations with a Business Intelligence tool. (They can explain the basics of MapReduce and big data analysis and visualization)

none

The module covers the following topics/contents:
Business Intelligence serves to collect, analyse and process information to improve decision making by decision makers. At Big Data, the focus is on data growth (data volume, data speed and data diversity). The two subject areas are directly linked and are central elements of data management.

• Business Intelligence Architecture
• ETL process, data warehouse
• Use of a Business Intelligence Tool
• Data modelling for BI
• Query languages
• Data storage concepts
• Evaluation methods
• Star scheme
• MapReduce, Big Data Analysis and Visualization

Lecture script as provided in the course (required)
Freiknecht J./ Papp S. (2018): Big Data in der Praxis: Lösungen mit Hadoop, Spark, HBase und Hive: Daten speichern, aufbereiten, visualisieren. 2., aktualisierte und erweiterte Auflage. Hanser Verlag.
Grossmann W./ Rinderle-Ma S. (2015): Fundamentals of Business Intelligence: Data-Centric Systems and Applications. Springer.
Mayer-Schönberger V./ Cukier K. (2014): Big Data: A Revolution That Will Transform How We Live, Work, and Think. Nachdruck. Eamon Dolan.

Integrated course - teaching & discussion, demonstration, practical examples in groups

Immanent examination character: presentation of group project, written/oral exam