mapreduce class - introduction

2
Hadoop classes offered byte-sized, 3 hours each, after work hours, conducted by industry veterans with a practical “from- the-trenches” approach. MapReduce Class – Introduction An exhaustive class which goes down to the basics of MapReduce where students will learn: Parallel processing, functional programming as the foundation for Hadoop. How map and reduce work. How map and reduce collaborate through shuffle. HDFS fundamentals. Input, output formats. Simple examples of Map Reduce with Java & Map Reduce with Streaming. Anatomy of a Hadoop job: Job submission & Execution. Instructor: Pranab Ghosh is a freelance consultant, currently working for Motorola, helping them process ever growing volume mobile device usage data, using Hadoop and other cloud technologies. For more than 25 years, he has worked with myriad of technologies and platforms in various business domains for early stage startups, large corporations and anything in between. He is an active blogger and open source contributor. His current interests are big data, distributed processing, NOSQL databases and data mining. Lab Work: Hands on lab exercises working with Big Data sets on a Hadoop cluster running on Amazon EC2.

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Hadoop classes offered byte-sized, 3 hours each, after work hours, conducted by industry veterans with a practical “from-the-trenches” approach.

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Page 1: MapReduce Class - Introduction

Hadoop classes offered byte-sized, 3 hours each, after work hours, conducted by industry veterans with a practical “from-

the-trenches” approach.

MapReduce Class – Introduction

An exhaustive class which goes down to the basics of MapReduce where students

will learn:

Parallel processing, functional programming as the foundation for Hadoop.

How map and reduce work.

How map and reduce collaborate through shuffle.

HDFS fundamentals. Input, output formats.

Simple examples of Map Reduce with Java & Map Reduce with Streaming.

Anatomy of a Hadoop job: Job submission & Execution.

Instructor:

Pranab Ghosh is a freelance consultant, currently working for Motorola,

helping them process ever growing volume mobile device usage data, using

Hadoop and other cloud technologies. For more than 25 years, he has

worked with myriad of technologies and platforms in various business

domains for early stage startups, large corporations and anything in

between. He is an active blogger and open source contributor. His current

interests are big data, distributed processing, NOSQL databases and data

mining.

Lab Work:

Hands on lab exercises working with Big Data sets on a Hadoop cluster

running on Amazon EC2.

Page 2: MapReduce Class - Introduction

Prerequisites :

Basic Linux command line skills.

Audience:

Developers, Data Analytics professionals, Business Analysts, Managers

Recommended Readings:

O’Reilly’s ‘Hadoop’ book by Tom White

Hadoop tutorial on YDN

Class Dates:

August 30th 2011

Class Timings:

6:00 pm to 9:00 pm

Class Location:

Third Eye’s Offices at

2900 Gordon Ave, Suite 100-20

Santa Clara, CA 95051

Price:

$150 - 25% off if bought two weeks before the class

$180 - 10% off if bought one week before the class

$200 - Full price if bought within less than a week

For further information, please contact:

Dj Das

[email protected]

408-431-1487

Register today!