Big Data Processing
level of course unit
Learning outcomes of course unit
Students are familiar with the special challenges associated with the storage and processing of large data volumes (5V model volume, variety, velocity, veracity, value). Furthermore, they are familiar with available options for countering these problems and able to independently develop and apply solutions with respect to a specific problem area.
prerequisites and co-requisites
Software Development for Data Science 1 and 2
Data Engineering for Data Science
Students are introduced to the basic properties of big data. Special emphasis is placed on handling this data while acquired knowledge is consolidated with examples. Suitable frameworks are introduced for solving big data problems and processed within the context of interactive workshops. Applicable examples:
These frameworks are to be explained and used based on case examples. Cen-trally provided data labs can be accessed for this purpose.
recommended or required reading
- EMC Education Services (2015) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. 1. Auflage, Wiley, Indianapolis (ISBN: 978-1118876138).
- O'Neil, C.; Schutt, R. (2013) Doing Data Science. Straight Talk from the Frontline. 1. Auflage, O'Reilly Media, Sebastopol (ISBN: 978-1449358655).
- Provost, F.; Fawcett, T. (2013) Data Science for Business: What you need to know about data mining and data-analytic thinking. 1. Auflage, O'Reilly Media, Sebastopol (ISBN: 978-1449361327).
- Narkhede, N.; Shapira, G.; Palino, T. (2017) Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale. 1. Auflage, O'Reilly Media, Farnham (ISBN: 978-1491936160).
- Jain, V. K. (2017) Big Data and Hadoop. 1. Auflage, Khanna Book Publishing, New Delhi (ISBN: 978-9382609131).
- Karau, H.; Warren, R. (2017) High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark. 1. Auflage, O'Reilly Media, Farnham (ISBN: 978-1491943205).
assessment methods and criteria
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
semester/trimester when the course unit is delivered
name of lecturer(s)
Head of studies
year of study
recommended optional program components
course unit code
type of course unit
mode of delivery