Comments on: Digging Deeper Into Data With Hadoop http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/ Fri, 10 Feb 2012 23:25:27 +0000 hourly 1 http://wordpress.com/ By: The Future Is Big Data in the Cloud http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213302 Thu, 19 Nov 2009 20:29:51 +0000 http://gigaom.com/?p=52525#comment-213302 [...] that was been deployed by some of the largest web properties (Yahoo, LinkedIn, Facebook, etc.) for massive parallel computation and distributed file systems in a cloud environment is Hadoop (disclosure: Accel is an investor in Cloudera, the company behind which provides commercial support [...]

]]> By: MW » The Future Is Big Data in the Cloud http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213301 Tue, 10 Nov 2009 10:36:25 +0000 http://gigaom.com/?p=52525#comment-213301 [...] that was been deployed by some of the largest web properties (Yahoo, LinkedIn, Facebook, etc.) formassive parallel computation and distributed file systems in a cloud environment is Hadoop (disclosure: Accel is an investor in Cloudera, the company behind Hadoop). In many cases, Hadoop [...]

]]> By: Hadoop Rocks http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213300 Thu, 11 Jun 2009 06:17:39 +0000 http://gigaom.com/?p=52525#comment-213300 Steve, nice segway leading to your product promotion.

]]> By: Yahoo! Releases Its Own Hadoop Distribution | CloudAve http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213299 Wed, 10 Jun 2009 23:50:27 +0000 http://gigaom.com/?p=52525#comment-213299 [...] articles by Zemanta Yahoo! exposes very own stuffed elephant code Digging Deeper Into Data With Hadoop Cloudera CEO: Hadoop Will Go Beyond Web Apps More Than Enterprise Scale, It Is Science Scale – [...]

]]> By: Steve Wooledge http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213298 Wed, 10 Jun 2009 00:54:35 +0000 http://gigaom.com/?p=52525#comment-213298 Gary,

Great article. We are certainly seeing interest for MapReduce implementations like Hadoop and Aster Data Systems beyond Web companies. The implications of lower cost ways to conduct compute-rich analysis on “Big Data” are tremendous.

It’s worth noting that the mainstream organization who are looking to harness the power of MapReduce for data-intensive applications, are also asking to leverage their existing IT ecosystem and skill-sets (given budget constraints). Aster Data is focusing on this by integrating MapReduce into our massively-parallel SQL database (which provides enterprise-class features IT organizations expect), as as well as enabling developers to write analytic functions and applications in any language they choose and have them execute in-database. This speeds performance, but also opens the door to a whole new class of developers to solve complex data problems.

Aster just announced support for Microsoft .NET within our In-Database MapReduce framework (the “other 1/2″ of custom application developers), which is a big step forward in bringing MapReduce to mainstream businesses:
http://www.asterdata.com/resources/downloads/whitepapers/Aster_MapReduce_Technical_Whitepaper.pdf

]]> By: Digging Deeper Into Data With Hadoop | google android os blog http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213297 Tue, 09 Jun 2009 00:16:27 +0000 http://gigaom.com/?p=52525#comment-213297 [...] What’s next for Hadoop, the open source software framework that helps process very large data sets? "We’re in the midst of a data-mining renaissance, and Hadoop is playing a leading role," writes Gay Orenstein on GigaOm. Hadoop recently helped the Yahoo! Developer Network set a new record in data sorting, and it is reaching other milestones. Check out the GigaOm story. [...]

]]> By: renaissance chambara alias Ged Carroll - Links of the day http://gigaom.com/2009/06/07/digging-deeper-into-data-with-hadoop/#comment-213296 Tue, 09 Jun 2009 00:03:53 +0000 http://gigaom.com/?p=52525#comment-213296 [...] Digging Deeper Into Data With Hadoop [...]

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