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Flightcaster and Hadoop Hadoop Bay Area User Group, March 24
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Shopping can be part of the travel experience or the primary focus of travel, major motivations for a leisurely travel trip. Tourists look for exciting opportunities to shop while travelling. For adventurous travel blog please visit http://wilsontom.blogspot.com/
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Building a Scalable Web Crawler with Hadoop by Ahad Rana from CommonCrawl Ahad Rana, engineer at CommonCrawl, will go over CommonCrawl’s extensive use of Hadoop to fulfill their mission of building an open, and accessible Web-Scale crawl. He will discuss their Hadoop data processing pipeline, including their PageRank implementation, describe techniques they use to optimize Hadoop, discuss the design of their URL Metadata service, and conclude with details on how you can leverage the crawl (using Hadoop) today.
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Empfohlen
Flightcaster and Hadoop Hadoop Bay Area User Group, March 24
Flightcaster_HUG
Flightcaster_HUG
guest3537ac
Shopping can be part of the travel experience or the primary focus of travel, major motivations for a leisurely travel trip. Tourists look for exciting opportunities to shop while travelling. For adventurous travel blog please visit http://wilsontom.blogspot.com/
Shopping Tourism
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wilson tom
Common crawlpresentation
Common crawlpresentation
Hadoop User Group
Hdfs high availability
Hdfs high availability
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Cascalog internal dsl_preso
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Karmasphere hadoop-productivity-tools
Karmasphere hadoop-productivity-tools
Hadoop User Group
Building a Scalable Web Crawler with Hadoop by Ahad Rana from CommonCrawl Ahad Rana, engineer at CommonCrawl, will go over CommonCrawl’s extensive use of Hadoop to fulfill their mission of building an open, and accessible Web-Scale crawl. He will discuss their Hadoop data processing pipeline, including their PageRank implementation, describe techniques they use to optimize Hadoop, discuss the design of their URL Metadata service, and conclude with details on how you can leverage the crawl (using Hadoop) today.
Building a Scalable Web Crawler with Hadoop
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Hdfs high availability
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Pig at LinkedIn by Chris Riccomini from LinkedIn Pig is an integral part of data analytics at LinkedIn. Learn about LinkedIn’s analytic stack, and see how Pig is used to design, develop, and deliver data products at LinkedIn. We’ll explore a successful example of Pig deployment at LinkedIn, pain points, and integration with Azkaban, Voldemort, Hadoop, and the rest of LinkedIn’s ecosystem.
Pig at Linkedin
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•Arun Murthy, from the Hadoop team at Yahoo! will introduce compendium of best practices for applications running on Apache Hadoop. In fact, we introduce the notion of a Grid Pattern which, similar to Design Pattern, represents a general reusable solution for applications running on the Grid. He will even cover the anti-patterns of applications running on the Apache Hadoop clusters. Arun will enumerate characteristics of well-behaved applications and provide guidance on appropriate uses of various features and capabilities of the Hadoop framework. It is largely prescriptive in its nature; a useful way to look at the presention is to understand that applications that follow, in spirit, the best practices prescribed here are very likely to be efficient, well-behaved in the multi-tenant environment of the Apache Hadoop clusters and unlikely to fall afoul of most policies and limits.
HUG August 2010: Best practices
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Pig at LinkedIn by Chris Riccomini from LinkedIn Pig is an integral part of data analytics at LinkedIn. Learn about LinkedIn’s analytic stack, and see how Pig is used to design, develop, and deliver data products at LinkedIn. We’ll explore a successful example of Pig deployment at LinkedIn, pain points, and integration with Azkaban, Voldemort, Hadoop, and the rest of LinkedIn’s ecosystem.
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•Arun Murthy, from the Hadoop team at Yahoo! will introduce compendium of best practices for applications running on Apache Hadoop. In fact, we introduce the notion of a Grid Pattern which, similar to Design Pattern, represents a general reusable solution for applications running on the Grid. He will even cover the anti-patterns of applications running on the Apache Hadoop clusters. Arun will enumerate characteristics of well-behaved applications and provide guidance on appropriate uses of various features and capabilities of the Hadoop framework. It is largely prescriptive in its nature; a useful way to look at the presention is to understand that applications that follow, in spirit, the best practices prescribed here are very likely to be efficient, well-behaved in the multi-tenant environment of the Apache Hadoop clusters and unlikely to fall afoul of most policies and limits.
HUG August 2010: Best practices
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2 hadoop@e bay-hug-2010-07-21
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1 content optimization-hug-2010-07-21
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3 avro hug-2010-07-21
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Public Terabyte Dataset Project: Web crawling with Amazon Elastic MapReduce
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Bhupesh Bansal, LinkedIn
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2 hadoop@e bay-hug-2010-07-21
2 hadoop@e bay-hug-2010-07-21
1 content optimization-hug-2010-07-21
1 content optimization-hug-2010-07-21
3 avro hug-2010-07-21
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1 hadoop security_in_details_hadoop_summit2010
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Public Terabyte Dataset Project: Web crawling with Amazon Elastic MapReduce
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Twitter Protobufs And Hadoop Hug 021709
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Hadoop and Voldemort @ LinkedIn
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Flightcaster Presentation Hadoop
1.
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3.
75% of major
delays less than 30 min. before departure
4.
5.
6.
Open Source hadoop
infer cascading -> cascading-clojure clojure crane
7.
8.
9.
Discrete Event Simulation? Fuzzy
Join with funky predicate?
10.
group-by with secondary
sort. accumulator to simulate a discrete event simultor.
11.
12.
cascading-clojure: motivation
FP dev deploy
13.
cascading-clojure: lessons learned
planner serialization data structures for wide data
14.
cascading-clojure: future direction
functions + data structures abstractions for planner and M/R
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