[ 926BUSIDAW13 ] Module Data Warehousing

Es ist eine neuere Version 2023W dieses Fachs/Moduls im Curriculum Master's programme Economic and Business Analytics 2023W vorhanden.
Workload Mode of examination Education level Study areas Responsible person Coordinating university
6 ECTS Accumulative module examination M1 - Master's programme 1. year Business Informatics Michael Schrefl Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Business Informatics 2022W
Objectives Students are able to use methods and tools to merge large amounts of data (Big Data), especially business and web data, in a data warehouse or data lake. The students master methods and tools for data analysis with data warehouses and data lakes, in particular OLAP languages. The students are familiar with the reference architecture of data warehouse and data lake systems and are proficient in planning, designing and implementing data warehouse and data lake systems, taking into account the volume, velocity and variety of the data to be analyzed.
Subject Reference architecture of data warehouse and data lake systems; multidimensional data model; conceptual, logical and physical design process for data warehouses and data lakes; techniques for extraction, cleansing and provisioning of business and web data; languages and tools for OLAP; security aspects; distributed data warehousing and cloud data warehousing; methods and tools for Big Data and real-time analytics, e.g. Hadoop, Map Reduce, Spark, Kafka.
Further information Literature:

  • Vaisman, A.; Zimányi, E.: Data Warehouse Systems: Design and Implementation. Springer, current edition.
  • Sherman, R.: Business Intelligence Guidebook: From Data Integration to Analytics. Morgan Kaufmann, current edition.
  • Gorelik, A.: The Enterprise Big Data Lake. O’Reilly, current edition.

Supplementary literature, especially on specific tools and systems, will be announced each semester.

Subordinated subjects, modules and lectures