Distributed Data Analytics (WT 2017/18) - tele-TASKhttps://tele-task.com/series/1179/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.High quality e-learning content created with tele-TASK - more than video! Powered by Hasso Plattner Institute (HPI)Thorsten PapenbrockThe 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.notele-TASKtele-task@hpi.deen℗; ©; tele-TASKSat, 16 Dec 2017 09:58:39 GMTPyRSS2Gen-1.1.0http://blogs.law.harvard.edu/tech/rssDistributed Systemshttps://tele-task.com/lecture/video/6587/01:34:50tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6587/Mon, 11 Dec 2017 16:25:17 GMTPartitioning & Transactionshttps://tele-task.com/lecture/video/6561/01:31:12tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6561/Wed, 06 Dec 2017 08:38:10 GMTReplicationhttps://tele-task.com/lecture/video/6548/01:23:21tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6548/Mon, 27 Nov 2017 15:00:45 GMTAkka Actor Programminghttps://tele-task.com/lecture/video/6530/01:23:26tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6530/Wed, 22 Nov 2017 14:08:24 GMTFormats for Encoding Data & Models of Dataflowhttps://tele-task.com/lecture/video/6502/01:32:38tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6502/Thu, 16 Nov 2017 16:37:16 GMTFoundations & Data Models and Query Languageshttps://tele-task.com/lecture/video/6428/01:31:23tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6428/Tue, 24 Oct 2017 14:15:52 GMTThe Document Data Model & The Graph Data Modelhttps://tele-task.com/lecture/video/6434/01:32:00tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6434/Tue, 24 Oct 2017 14:15:52 GMTStorage and Retrievalhttps://tele-task.com/lecture/video/6465/01:06:08tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6465/Tue, 24 Oct 2017 14:15:52 GMTIntroduction & Foundationshttps://tele-task.com/lecture/video/6413/01:29:32tele-TASK, HPI, computer science, technology, Germany, Potsdamhttps://tele-task.com/lecture/video/6413/Thu, 19 Oct 2017 09:53:14 GMT