Introduction to Hadoop (the how)
The Hadoop MapReduce API
Learn how to get started writing programs against Hadoop’s API.
Introduction to MapReduce Algorithms
Writing programs for MapReduce requires analyzing problems in a new way. This lecture shows how some
common functions can be expressed as part of a MapReduce pipeline.
Debugging MapReduce programs
Debugging in the distributed environment is challenging. This lecture will expose you to best practices for program
design to mitigate debugging challenges, as well as local testing tools and techniques for debugging at scale.
Optimizing MapReduce Programs
We’ll use the Cloudera Training VM to work through an example where you write a MapReduce program and
improve its performance using techniques explored earlier.
NOTE: Attendees should download the Cloudera Training vm from http://cloudera.com/hadoop-training-virtual-machine. VMWare Player (windows, linux) or VMWare Fusion (OS X) will be required in order to use it.
People planning to attend this session also want to see:
Aaron Kimball
Cloudera, Inc.
Aaron Kimball is a software engineer at Cloudera, Inc., the Commercial Hadoop company. Aaron is the principle developer of Sqoop, the SQL-to-Hadoop database import/export tool. Aaron has been working with Hadoop since early 2007, and contributes actively to its development. Through Cloudera, he additionally provides training to developers and system administrators working with Hadoop. Aaron holds a B.S. in Computer Science from Cornell University, and an M.S. in Computer Science and Engineering from the University of Washington.
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Comments
Two most useful explanations: 1) MapRed algorithms 2) Debugging MapRed jobs.
Great presentation.
I really enjoyed the explanation of the different map reduce algorithms (ie using map/reduce for joins).