Big Data Processing
Niveau
Master's program
Learning outcomes of the courses/module
The participants:
• can name the particular challenges that arise when storing and processing large volumes of data (V model: Volume, Variety, Velocity, Veracity)
• can name ways of meeting these challenges (systems from the respective areas of the V model are discussed here as examples)
• can develop and apply appropriate solutions themselves with a specific problem in mind
• can name the particular challenges that arise when storing and processing large volumes of data (V model: Volume, Variety, Velocity, Veracity)
• can name ways of meeting these challenges (systems from the respective areas of the V model are discussed here as examples)
• can develop and apply appropriate solutions themselves with a specific problem in mind
Prerequisites for the course
No prerequisites
Course content
Students are introduced to the fundamental characteristics of big data. Particular attention is paid to how such data is handled, and the knowledge acquired is reinforced with examples. Suitable frameworks are presented for solving big data problems and are worked on with case studies in interactive workshops. Examples include:
- Apache Spark
- Apache Flink
- Apache Storm
- Apache Samza
- Apache Kafka
These frameworks are explained and applied using case examples. The centrally provided data labs can be used for this purpose.
- Apache Spark
- Apache Flink
- Apache Storm
- Apache Samza
- Apache Kafka
These frameworks are explained and applied using case examples. The centrally provided data labs can be used for this purpose.
Recommended specialist literature
- Kleppmann, M.; Riccomini, C. (2026): Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (Ed. 2), O'Reilly, (ISBN: 978-1098119065)
- Chambers, B. (2018): Spark: The Definitive Guide: Big Data Processing Made Simple, O'Reilly, (ISBN: 978-1491912218)
- Marz, N.; Warren, J. (2015): Big Data: Principles and best practices of scalable realtime data systems (Ed. 1), Manning, (ISBN: 978-1617290343)
- Chambers, B. (2018): Spark: The Definitive Guide: Big Data Processing Made Simple, O'Reilly, (ISBN: 978-1491912218)
- Marz, N.; Warren, J. (2015): Big Data: Principles and best practices of scalable realtime data systems (Ed. 1), Manning, (ISBN: 978-1617290343)
Assessment methods and criteria
Written exam
Language
German
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Group work
- Interactive workshop
- Lecture with discussion
- Group work
- Interactive workshop
Semester/trimester in which the course/module is offered
3