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Yahoo! and Cloudera have worked together to produce a couple of training videos for Pig. There is Introduction to Pig, a 50 minute talk on Pig, including copious examples of writing Pig Latin scripts, an overview of how Pig works, and a discussion of the advantages of Pig versus other Hadoop interfaces

Pig Training Available Online Yahoo! and Cloudera have worked together to produce a couple of training videos for Pig. There is Introduction to Pig, a 50 minute talk on Pig, including copious examples of writing Pig Latin scripts, an overview of how Pig works, and a discussion of the advantages of Pig versus other Hadoop interfaces

One of the questions asked by prospective Hadoopers is whether Java is a prerequisite to learn Hadoop. Most of the learners exhibit some kind of disappointment when they ask this question - they feel not knowing Java to be a limitation and they might have to miss on a great career opportunity.

Today we are introducing Amazon Elastic MapReduce , our new Hadoop-based processing service. I'll spend a few minutes talking about the generic MapReduce concept and then I'll dive in to the details of this exciting new service.

Amazon Elastic MapReduce is a web service that enables businesses, researchers, data analysts, and developers to easily and cost-effectively process vast amounts of data. It utilizes a hosted Hadoop framework running on the web-scale infrastructure of Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3).

serve very large indexes as index shards on many servers

Exclusive Q&A with Shelton Shugar, SVP Cloud Computing, Yahoo! Ultimately, we believe that advancement in cloud computing technology will be driven by open source initiatives where large communities of engineers can collaborate and develop new code for the new applications and demands posed by the cloud model

The Fair Scheduler gives each user a configurable share of the cluster when he/she has running jobs, but assigns these resources to other users when the user is inactive.

In a web-driven world, datasets are larger than ever before - with "web scale" becoming the term of choice to describe the ultimate size of problems.