Big Data Engineering and Analysis

Big Data Engineering and Analysis (BDEA)

6/7. Semester 5 ECTS | 4 SWS Continuous Assessment (CA)

CSB IB IMB UIB

Big Data Engineering and Analytics: Analyze massive datasets, develop high-performance systems, and use modern tools for large-scale data processing and analysis.

What Is Big Data (the 4 Vs) and What Does Scalability Mean?
Big Data Architectures (e.g., Lambda, Microservices) and Their Design Principles
CAP Theorem, BASE vs. ACID, and Their Architectural Implications
Big Data Frameworks (Batch vs. Streaming: Hadoop, Spark, Flink) and Their Practical Use
NoSQL Databases: Selection Criteria and Usage (e.g., Cassandra and Kafka)
Big Data Algorithms (e.g., HyperLogLog, Bloom Filters)
Data Analysis using the tools mentioned before

Competencies

Students are able to

identify the challenges involved in processing large amounts of data
select appropriate methods and tools for scalable solutions
analyze and structure parallel and distributed systems
and design and implement big data systems of moderate complexity

Literature

O. Hummel, M. Kessel, B. Navarro und R. Butscher, Data Science mit Big Data: Techniken, Werkzeuge und Algorithmen zur Analyse großer Datenmengen. München: Carl Hanser Verlag, 2026.
J. Leskovec, A. Rajaraman und J. Ullman, Mining of Massive Datasets. Cambridge, U.K.: Cambridge University Press, 2014.
M. Kleppmann, Designing Data-Intensive Applications. Sebastopol, CA, USA: O’Reilly Media, 2017.
N. Marz und J. Warren, Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. Shelter Island, NY, USA: Manning Publications, 2015.
R. Baeza-Yates und B. Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technology Behind Search, 2. Aufl. New York, NY, USA: ACM Press, 2010.

Lecturer

Prof. Dr. Oliver Hummel
Dr. Marcus Kessel

Recommended Previous Knowledge

Data Management (DM)
Programming 1 (PR1)
Programming 2 (PR2)
Parallel Programming (PP)
Don't be afraid of programming, algorithms, and a little statistics

Module Details

Semester	6/7
Lecture Language	German
Frequency	Not regularly
Credit Points (ECTS)	5
Course Coordinator	Prof. Dr. Oliver Hummel
Duration	1 Semester
Course Achievement	None
Prerequisite for exam	None
Exam	Continuous Assessment (CA)

Weekly Hours (SWS)

Lecture	2 SWS
Exercises	1 SWS
Lab	1 SWS
Sum	4 SWS

Work Load

Lecture	25 h
Lab	25 h
Self Study	25 h
Assignments	25 h
Exam Preparation	50 h
Sum	150 h