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Data Engineering
level of course unit
1st semester: Master's course / 2nd semester: Master's courseLearning outcomes of course unit
The following skills are developed in the course:
- Students are familiar with various advanced data storage concepts (e.g. NoSQL databases, distributed databases, etc.).
- Students can compare and select data storage concepts with regard to their suitability for projects.
- Students understand the special requirements for data storage resulting from the use of very quantities amounts of data (Big Data).
- Students are familiar with various advanced data storage concepts (e.g. NoSQL databases, distributed databases, etc.).
- Students can compare and select data storage concepts with regard to their suitability for projects.
- Students understand the special requirements for data storage resulting from the use of very quantities amounts of data (Big Data).
prerequisites and co-requisites
1st semester: Students will have previous knowledge in the field of information technologies to the extent of 6 ECTS and therefore know the concept of the relational database and can read simple SQL queries. / 1st semester: Students will have previous knowledge in the field of information technologies to the extent of 6 ECTS and therefore know simple programming concepts (e.g. variables, branches, loops) as well as typical programming approaches (e.g. functional programming). / 2nd semester: SDDE.A1 module examination (Software Development 1)
course contents
The following content is discussed in the course:
- Properties of high-performance data systems (scalability, maintainability, reliability)
- Established concepts of data storage (Relational Model)
- Historical concepts of data storage (Hierarchical Model, Network Model)
- Modern concepts of data storage (Wide-Column Model, Graph Model, Key-Value Model, Document Model, Column-Oriented Model)
- Database systems, matching the models discussed
- Scaling of data systems (replication and partitioning)
- Writing and reading in data systems (index structures, write strategies)
- Properties of high-performance data systems (scalability, maintainability, reliability)
- Established concepts of data storage (Relational Model)
- Historical concepts of data storage (Hierarchical Model, Network Model)
- Modern concepts of data storage (Wide-Column Model, Graph Model, Key-Value Model, Document Model, Column-Oriented Model)
- Database systems, matching the models discussed
- Scaling of data systems (replication and partitioning)
- Writing and reading in data systems (index structures, write strategies)
recommended or required reading
PRIMARY LITERATURE:
- Kleppmann, M. (2017): Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintain-able Systems (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449373320)
SECONDARY LITERATURE:
- Celko, J. (2013): Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases (Ed. 1), Morgan Kaufmann, Waltham (ISBN: 978-0124071926
- Kleppmann, M. (2017): Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintain-able Systems (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449373320)
SECONDARY LITERATURE:
- Celko, J. (2013): Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases (Ed. 1), Morgan Kaufmann, Waltham (ISBN: 978-0124071926
assessment methods and criteria
Written exam
language of instruction
Germannumber of ECTS credits allocated
4eLearning quota in percent
50course-hours-per-week (chw)
2planned learning activities and teaching methods
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Interactive workshop
- Lecture with discussion
- Processing of exercises
- Interactive workshop
