Distributed Data Management (WT 2019/20)

Thorsten Papenbrock

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.

Predecessor of this series: Distributed Data Management (WT 2018/19)



Date: October 14, 2019
Language: English
Duration: 01:15:38
Views: 167


Date: October 15, 2019
Language: English
Duration: 01:21:37
Views: 90

Encoding and Communication

Date: October 21, 2019
Language: English
Duration: 01:29:36
Views: 105

Encoding and Communication 2

Date: October 22, 2019
Language: German
Duration: 01:31:35
Views: 114

Akka Actor Programming

Date: October 28, 2019
Language: English
Duration: 01:31:24
Views: 139

Akka Actor Programming 2

Date: November 4, 2019
Language: English
Duration: 01:30:36
Views: 66

Akka Actor Programming 3 - Patterns

Date: November 5, 2019
Language: English
Duration: 01:31:16
Views: 72

Data Models and Query Languages

Date: November 11, 2019
Language: English
Duration: 01:28:29
Views: 21