Distributed Data Analytics (WT 2017/18)

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.

Introduction

Introduction & Foundations

Date: October 18, 2017
Language: English
Duration: 01:29:32

Data Models and Query Languages

Foundations & Data Models and Query Languages

Date: October 25, 2017
Language: English
Duration: 01:31:23

The Document Data Model & The Graph Data Model

Date: November 1, 2017
Language: English
Duration: 01:32:00

Storage and Retrieval

Encoding and Evolution

Akka Actor Programming

Date: November 22, 2017
Language: English
Duration: 01:23:26

Replication

Replication

Date: November 29, 2017
Language: English
Duration: 01:23:21

Partitioning & Transactions

Partitioning & Transactions

Date: December 6, 2017
Language: German
Duration: 01:31:12

Distributed Systems

Distributed Systems

Date: December 13, 2017
Language: English
Duration: 01:34:50