As\u00a0 Armando Fox<\/a>, a professor of computer science at the University of California, Berkeley who specializes in these sorts of data-center-sized software platforms. put it in a Wired article<\/a>:<\/p>\n “This is unprecedented. Hadoop is the centerpiece of the \u201cBig Data\u201d movement, a widespread effort to build tools that can analyze extremely large amounts of information. But with today\u2019s Big Data tools, there\u2019s often a drawback. You can\u2019t quite analyze the data with the speed and precision you expect from traditional data analysis or \u201cbusiness intelligence\u201d tools. But with Dremel, Fox says, you can.<\/em><\/p>\n \u201cThey managed to combine large-scale analytics with the ability to really drill down into the data, and they\u2019ve done it in a way that I wouldn\u2019t have thought was possible,\u201d he says. \u201cThe size of the data and the speed with which you can comfortably explore the data is really impressive. People have done Big Data systems before, but before Dremel, no one had really done a system that was that big and that fast.<\/em><\/p>\n \u201cUsually, you have to do one or the other. The more you do one, the more you have to give up on the other. But with Dremel, they did both.\u201d<\/em><\/p>\n First Google created, and wrote papers on, Hadoop and MapReduce, which got reverse-engineered into the current state of the art for Big Data. But Google has moved on to Dremel, and the rest of the world is slow in catching up. With BigQuery Google offers a simple-to-user service that doesn’t sacrifice Big Data scale OR […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[286,156,289,226,821,822,344,290,342],"tags":[],"class_list":["post-3148","post","type-post","status-publish","format-standard","hentry","category-apache-hadoop","category-big-data","category-bigdata","category-business-intelligence","category-data-analysis","category-dremel","category-google","category-hadoop","category-mapreduce"],"_links":{"self":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/3148","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/comments?post=3148"}],"version-history":[{"count":0,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/3148\/revisions"}],"wp:attachment":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/media?parent=3148"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/categories?post=3148"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/tags?post=3148"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"http:\/\/img.zemanta.com\/pixy.gif?x-id=bf54ce63-44c1-4e8d-ae74-df0c919e4479\")
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