Data Science & Intelligent Analytics PT
Apply Icon
Apply
now

Dat Engineering Lab

level of course unit

Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- Students can implement storage concepts themselves in the context of a specific problem.
- Students are also able to design the implementation of these systems with regard to scalability and operational requirements.

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: Stu-dents 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:

- Design and implementation of problem-centred NoSQL databases (e.g. key-value stores, document stores, column-oriented data stores, etc.)
- Design and implementation of storage solutions for large quantities of data (big data)

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)

assessment methods and criteria

The following examination methods are used in the course:

- Project work
- term paper

language of instruction

German

number of ECTS credits allocated

5

eLearning quota in percent

0

course-hours-per-week (chw)

2

planned learning activities and teaching methods

The following methods are used:

- Processing of exercises
- Lecture with discussion

semester/trimester when the course unit is delivered

1

name of lecturer(s)

Prof. (FH) Dr. Michael Kohlegger

year of study

1

recommended optional program components

none

course unit code

SDDE.3

type of course unit

practice

mode of delivery

Compulsory

work placement(s)

none