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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 2025W |
| Learning Outcomes |
Competences |
| By applying the current scientific state of the art, students are able to plan, design and implement data warehouse and data lake systems, taking into account the volume, velocity and variety of the data to be analyzed. Students are able to merge large internal and external data stocks (big data) in a data warehouse or data lake in order to use them for interactive data analyses in accordance with the company's objectives.
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| Skills |
Knowledge |
- LO2: Students master the design of an architecture for a data warehouse or data lake system and can compare and evaluate different alternatives (K4).
- LO3: Students are proficient in the conceptual and logical modeling of data in a data warehouse system and can compare and evaluate different models (K4).
- LO4: Students are able to formulate analytical queries using a data warehouse system and provide data for analyses and reports (K3).
- LO5: Students can perform the physical optimization of data storage by defining materialized views, indices, partitioning and distributed data storage, compare and evaluate different optimizations (K3).
- LO6: Students can implement data pipelines that include the extraction of data from internal and external data sources, their cleansing and transformation and the insertion of the cleansed and transformed data into a data warehouse system (K3).
- LO7: Students can implement distributed and parallel analysis processes for large amounts of data (big data) and processes for the real-time analysis of data streams (K3).
- LO8: Students understand security, data governance and management aspects when designing data warehouse and data lake systems in the context of business intelligence projects (K2).
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LO1: Reference architectures of data warehouse and data lake systems; multidimensional data model; logical data model (star/snowflake schema and mixed variants, data vault); conceptual, logical and physical design process for data warehouses and data lakes; methods for selecting materialized views; Algorithms for managing materialized views; index structures for data warehouse systems; partitioning methods and optimization of queries on partitioned data; methods and tools for extracting, transforming and loading internal and external data, e.g. KNIME, dbt. e.g. KNIME, dbt; methods and tools for the provision of data analysis, e.g. KNIME, Apache Superset; languages and tools for OLAP, e.g. SQL, MDX; security aspects; distributed data warehousing and cloud data warehousing; methods and tools for big data and real-time analytics, e.g. Apache Hadoop, Map Reduce, Apache Spark, Apache Kafka
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| Criteria for evaluation |
Written exam
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| Methods |
The course content is taught using blended learning methods, in particular flipped classroom, with interactive elements to consolidate knowledge.
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| Language |
German/English |
| Study material |
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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| Changing subject? |
No |
| Further information |
VL and UE Data Warehousing form an inseparable didactic unit. The learning outcomes described are achieved through the interaction of both.
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WBMDWV: VL Data Warehousing (2008W-2014S)
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