Prof. Dr. Felix Naumann

Distributed Data Management

Lecture: Prof. Dr. Felix Naumann & Dr. Thorsten Papenbrock
The lectures will be held in English.


The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today's CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization.

Big Data Analytics is a multi-million dollar market that grows constantly! Data and the ability to control and use it is the most valuable ability of today's computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements.

In this lecture, we take a look a various technologies involved in building distributed, data-intensive systems. We discuss theoretical concepts (data models, encoding, replication, ...) as well as some of their practical implementations (Akka, MapReduce, Spark, ...). Since workload distribution is a concept which is useful for many applications, we focus in particular on data analytics.

Related Topics:


Topic list

  • Foundations of Distributed Data Management
  • OLAP and OLTP
  • Encoding and Evolution
  • Hands-on Akka
  • Data Models and Query Languages
  • Storage and Retrieval
  • Replication
  • Partitioning
  • Batch Processing
  • Hands-on Spark
  • Distributed Systems
  • Consistency and Consensus
  • Transaction Processing
  • Stream Processing
  • Hands-on Flink
  • Services and Containerization
  • Cloud-based Data Systems


Thelecure is taking place Mondays at 1:30 PM, HS 3 and Tuesday at 09:15 AM, HS 2 Campus I (find individual room changes below). Recordings of the lecture are available on tele-TASK.

16.10. in Hörsaal 2Foundations
22.10.Distributed DBMS
23.10. in H 2.57Distributed DBMS
29.10.Encoding and Evolution
30.10.Encoding and Evolution
05.11.Hands-on Akka Actor Programming (students.csv / SerialAnalyzer.java)
06.11.Hands-on Akka Actor Programming
12.11.Hands-on Akka Actor Programming
13.11.Data Models and Query Languages
19.11.Data Models and Query Languages
20.11.Storage and Retrievel
03.12.Batch Processing
04.12.Batch Processing
10.12.Hands-on: Spark (TPCH.zip)
11.12.Hands-on: Spark
17.12.Hands-on: Spark
18.12.Distributed Systems
Christmas break
07.01.Consistency and Consensus
14.01.Stream Processing
15.01.Stream Processing
17.01. (Thursday)Flink Hands-on with Data Artisans
21.01.Distributed Query Optimization
22.01.Distributed Query Optimization
28.01.no lecture
29.01.no lecture
04.02.Exercise Evaluation
05.02.Lecture Summary (Exam1718, Exam1718Solution)



The grade will determined in an exam. The exam will take place on February 11 in the time from 09:00 to 12:00 AM in HS 1. The prerequisite for admission to the exam is the successful completion of the exercises.


Course book:

  • Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017, 978-1449373320

Further reading:

  • Distributed Systems, Maarten van Steen and Andrew S. Tanenbaum, 2017, 978-1543057386
  • Web-Scale Data Management for the Cloud, Wolfgang Lehner and Kai-Uwe Sattler, 2013, 1489997717
  • Introduction to Parallel Computing, Zbigniew J. Czech, 2017, 978-1107174399
  • Principles of Distributed Database Systems, M. Tamer Özsu and Patrick Valduriez, 2011, 978-1441988331
  • Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services, Brendan Burns, 2017, 978-1491983645
  • Spark: Big Data Cluster Computing in Production, Ilya Ganelin and Ema Orhian and Kai Sasaki and Brennon York, 2016, 978-1119254010
  • Reactive Messaging Patterns with the Actor Model, Vaughn Vernon, 2015, 978-0133846836
  • Mining Massive Datasets, Jure Leskovec and Anand Rajaraman and Jeffrey David Ullman, 2014, 978-1107077232
  • Algorithmische Geometrie, Rolf Klein, 2005, 978-3540209560