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Data modeling and storage

Niveau

Second cycle, master’s degree

Learning outcomes of the courses/module

The participants:
• can explain central concepts of data modeling
• can independently develop data models for a given scenario
• can describe different data storage solutions
• can compare storage solutions with regard to their suitability for a given scenario
• can identify appropriate AI tools for data modeling
• can determine specific considerations for data modeling and storage for AI systems

Prerequisites for the course

Not applicable

Course content

- Data modeling for relational data structures
- Database interaction in SQL (DDL, DML, DQL)
- Non-relational data storage concepts (NoSQL databases)
- Implementation of data structures
- Integration of data structures into applications

Recommended specialist literature

PRIMÄRLITERATUR:
- Kleppmann, M. (2017): Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449373320)

SEKUNDÄRLITERATUR:
- Daniel, K. (2026): NoSQL Handbook for Modern Developers: A Practical Guide to Build, Scale, and Secure Cloud-Native Applications, Independently published (ISBN: 979-8245616483)

Assessment methods and criteria

Written exam

Language

German

Number of ECTS credits awarded

6

Semester hours per week

Planned teaching and learning method

The following methods are used:

- Lecture with discussion
- Completion of practice exercises
- Interactive workshop

Semester/trimester in which the course/module is offered

1

Type of course/module

Type of course