The main goal of the Data Engineering course is threefold.

Firstly, students will learn the fundamental principles and techniques underpinning off-line data engineering and integration. This involves data modeling with ER/relational models, object data models, NoSQL data models, and integration techniques to synthesize them.

Secondly, students will learn the basic principles and mechanisms to develop run-time data integration, storage and processing. This includes: distributed (cloud) computing, distributed database architectures, batch/stream processing, complex event processing, and runtime data integration.

Lastly, at the end of this course, course participants will be able to judge from an architectural (conceptual) and implementation perspective, when which off- or/and online solution suits best to address particular practical challenges.

 

The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program.

Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS).

 

1. Zomaya, Albert Y., and Sherif Sakr, eds. Handbook of Big Data Technologies. Berlin: Springer, 2017.
2. Articles will be posted on Blackboard during the semester, creating a ”living” Syllabus.

Other useful texts:

1. Kleppmann, Martin. Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. " O'Reilly Media, Inc.", 2017.
2. Gregor Hohpe and Bobby Woolf., Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, 1st Edition, 2004, ISBN 978-0321200686.