Distributed Data Management (ST 2021) - 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 and management are a multi-million dollar markets that grow constantly! The ability to control and utilize large amounts of data 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 at various technologies involved in building distributed, data-intensive systems. We start by discussing fundamental concepts in distributed computing, such das data models, encoding formats, messaging, data replication and partitioning, fault tollerance, and batch- and stream processing. In between, we consider different practical systems from the Big Data Landscape, such as Akka and Spark. In the end, we concentrate on data management aspects, such as distributed database management system architectures and distributed query optimization.
Distributed Data Management (ST 2021) - tele-TASK

馃帶 Podcast episodes

Listen to 26 episodes
Lecture Summary
117 minutes

Lecture Summary

Federated DBMSS
76 minutes

Federated DBMSS

Stream Processing
80 minutes

Stream Processing

Exercise 1 Evaluation
89 minutes

Exercise 1 Evaluation

Spark Batch Processing (2)
98 minutes

Spark Batch Processing (2)

Spark Batch Processing
90 minutes

Spark Batch Processing

Batch Processing
91 minutes

Batch Processing

Transactions
87 minutes

Transactions

Consistency and Consensus
101 minutes

Consistency and Consensus

Distributed Systems
81 minutes

Distributed Systems

Replication & Partitioning
82 minutes

Replication & Partitioning

Replication
95 minutes

Replication

Storage and Retrieval
95 minutes

Storage and Retrieval

Data Models and Query Languages
94 minutes

Data Models and Query Languages

Akka Actor Programming 2
93 minutes

Akka Actor Programming 2

Akka Actor Programming
93 minutes

Akka Actor Programming

Communication
84 minutes

Communication

Encoding
94 minutes

Encoding

Foundations
82 minutes

Foundations

Introduction
94 minutes

Introduction

Similar podcasts

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

Distributed Data Management (WT 2018/19) - tele-TASK

Distributed Data Analytics (WT 2017/18) - tele-TASK

Hardware-Conscious Data Processing (ST 2022) - tele-TASK

Big Data Systems (WT 2019/20) - tele-TASK

Internet Security (WT 2018/19) - tele-TASK

CLINICAL DATA MANAGEMENT

St. Andrew Going Backstage 2021