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

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Prof. Dr. Felix Naumann, Dr. Thorsten Papenbrock

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Podcast data

Created By Prof. Dr. Felix Naumann, Dr. Thorsten Papenbrock

Podcast Status active

Started 15/10/2018

Latest Episode 05/02/2019

Release Period Episodic

Episodes 26

Partner Reviews 1

Language English

Frequency 132

Average Length 86 minutes and 15 seconds

Country United States

Global Rank TOP 1%

Description

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.
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Episodes
- #695632 - Lecture Summary
  
  6 years ago
- #695633 - Distributed Query Optimization (1)
  
  6 years ago
- #695634 - Distributed Query Optimization (2)
  
  6 years ago
- #695635 - Processing Streams
  
  6 years ago
- #695636 - Stream Processing
  
  6 years ago
Reviews
- There are no reviews yet
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Distributed Data Management (WT 2018/19) - tele-TASK

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

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Podcast data

Description

Agent Outreach

Episodes

#695632 - Lecture Summary

#695633 - Distributed Query Optimization (1)

#695634 - Distributed Query Optimization (2)

#695635 - Processing Streams

#695636 - Stream Processing

Reviews

Podcasts like this

Wharton FinTech Podcast

Wharton FinTech Podcast

Data Stories

Data Stories

In Machines We Trust

In Machines We Trust

The Local Maximum

The Local Maximum

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