Inhalt
[ 926BUSIDAW13 ] Module Data Warehousing
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| (*) Unfortunately this information is not available in english. |
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| 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 |
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| Detailed information |
| Pre-requisites |
(*)Erwartete Vorkenntnisse: Grundlagen relationaler Datenbanksysteme
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| Original study plan |
Master's programme Business Informatics 2023W |
| 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.
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| 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.
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| 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.
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