Data Science & Intelligent Analytics PT
Apply Icon
Apply
now

Big Data Processing

level of course unit

Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- The students are familiar with the special challenges involved in storing and processing large quantities of data (V-model: Volume, Variety, Velocity, Veracity).
- Students know the options for meeting these challenges (exemplary systems from the respective areas of the V-model are discussed).
- Students can develop and apply appropriate solutions to a specific problem.

prerequisites and co-requisites

3rd semester: No prerequisites

course contents

Students are introduced to the basic features of Big Data. Special attention is paid to the handling of this data and the knowledge acquired is consolidated with examples. Suitable frameworks for solving Big Data problems are presented and worked on in interactive workshops with case studies. Examples of this are as follows:

- Apache Hadoop
- Apache Spark
- Apache Flink
- Apache Storm
- Apache Samza
- Apache Kafka

These frameworks will be explained and used with case studies. For this purpose, the centrally-provided Data Labs can be accessed.

recommended or required reading

PRIMARY LITERATURE:
- Jain, V. K. (2017): Big Data and Hadoop (Ed. 1), Khanna Book Publishing, New Delhi (ISBN: 978-9382609131)
- Karau, H.; Warren, R. (2017): High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1491943205)

SECONDARY LITERATURE:
- O'Neil, C.; Schutt, R. (2013): Doing Data Science. Straight Talk from the Frontline (Ed. 1), O'Reilly Media, Sebastopol (ISBN: 978-1449358655)
- Narkhede, N.; Shapira, G.; Palino, T. (2017): Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1491936160)

assessment methods and criteria

Written exam

language of instruction

English

number of ECTS credits allocated

4

eLearning quota in percent

25

course-hours-per-week (chw)

2

planned learning activities and teaching methods

The following methods are used:

- Lecture with discussion
- Group work
- Interactive workshop

semester/trimester when the course unit is delivered

3

name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

course unit code

DPR.1

type of course unit

integrated lecture

mode of delivery

Compulsory

work placement(s)

none