Big Data Processing

Master's degree program

The participants:

• can identify the particular challenges that arise in storing and processing large volumes of data (V model: volume, variety, velocity, veracity)
• can identify ways to address 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 view to a specific problem

No prerequisites

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 through examples. To solve big data problems, suitable frameworks are presented and worked on in interactive workshops using case studies. Examples of these include:

- Apache Hadoop
- 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.

- 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)

Written exam

English

4

The following methods are used:

- Lecture with discussion
- Group work
- Interactive workshop

3