{"id":186622,"date":"2011-08-04T00:00:00","date_gmt":"2011-08-05T16:32:54","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/madder-and-self-tuning-data-analytics-on-hadoop-with-starfish\/"},"modified":"2016-08-22T11:30:47","modified_gmt":"2016-08-22T18:30:47","slug":"madder-and-self-tuning-data-analytics-on-hadoop-with-starfish","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/madder-and-self-tuning-data-analytics-on-hadoop-with-starfish\/","title":{"rendered":"MADDER and Self-Tuning Data Analytics on Hadoop with Starfish"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Timely and cost-effective analytics over &#8220;big data&#8221; is now a key ingredient for success in businesses and scientific disciplines. The Hadoop platform&#8212;consisting of an extensible MapReduce execution engine, pluggable distributed storage engines, and a range of procedural to declarative interfaces to express analysis tasks&#8212;is an emerging choice for big data analytics. Hadoop&#8217;s performance out of the box can be poor, causing suboptimal use of resources, time, and money (e.g., in pay-as-you-go clouds). Unfortunately, practitioners of big data analytics such as business analysts, computational scientists, and researchers often lack the expertise to tune the Hadoop platform for good performance.<\/p>\n<p>I will introduce Starfish, a self-tuning system for big data analytics.<br \/>\nStarfish builds on Hadoop, while adapting to system workloads and user needs to provide good performance automatically; without any need for users to understand and manipulate the many tuning knobs in the Hadoop platform. While Starfish&#8217;s design is guided by work on self-tuning database systems, I will discuss how new analysis practices (dubbed the MADDER principles) over big data pose new challenges; leading us to different design choices in Starfish. Starfish is under active development and is available at: http:\/\/www.cs.duke.edu\/starfish<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Timely and cost-effective analytics over &#8220;big data&#8221; is now a key ingredient for success in businesses and scientific disciplines. The Hadoop platform&#8212;consisting of an extensible MapReduce execution engine, pluggable distributed storage engines, and a range of procedural to declarative interfaces to express analysis tasks&#8212;is an emerging choice for big data analytics. Hadoop&#8217;s performance out of [&hellip;]<\/p>\n","protected":false},"featured_media":196301,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-186622","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/E211V_3iwOo","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186622","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186622\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/196301"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=186622"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=186622"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=186622"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=186622"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=186622"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=186622"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=186622"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=186622"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=186622"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=186622"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}