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Distributed Data Management (WT 2019/20) - tele-TASK

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.

Distributed Data Management (WT 2019/20) - tele-TASK

Exam Preparation

Distributed DBMSs & Distributed Query Optimization

Distributed DBMSs

Stream Processing & Distributed DBMSs

Stream Processing

Spark Batch Processing & Stream Processing

Spark Batch Processing 2

Spark Batch Processing

Exercise Evaluation Assignment 1-3

Beyond MapReduce

Batch Processing: Distributed File Systems and MapReduce

Transactions & Batch Processing

Consistency and Consensus & Transactions

Distributed Systems & Consistency and Consensus

Distributed Systems

Replication & Partitioning

Replication 2

Storage and Retrieval & Replication

The Graph Data Model

Data Models and Query Languages

Akka Actor Programming 3 - Patterns

Akka Actor Programming 2

Akka Actor Programming

Encoding and Communication 2

Encoding and Communication

Foundations

Introduction