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    <h1>Data-Intensive Computing with MapReduce<br/><small>INFM 718G / CMSC 828G (Spring 2013)</small></h1>
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<p>
<b>Time:</b> Thursday, 6:00-8:45pm<br/>
<b>Location:</b> HBK 2119<br/>
<b>Instructor:</b> Jimmy Lin (jimmylin@umd.edu)
</p>

<p>Our world is being revolutionized by "big data": petabyte-scale
data stores are popping up everywhere, opening up exciting
opportunities for computing applications and scientific
discovery. Data-intensive computing requires programming models that
allow us to easily distribute computations across large clusters, and
this is where MapReduce comes in. MapReduce, especially the Hadoop
open-source implementation, has recently emerged as a popular
framework for data-intensive computing. Among its advantages include
the ability to horizontally scale to petabytes of data on thousands of
commodity servers, easy-to-understand programming semantics, and a
high degree of fault tolerance. Hadoop lies at the core of an
application stack that is gaining widespread adoption in both industry
and academia.</p>

<p>This course will provide an introduction to Hadoop, focusing
specifically on algorithm design and "thinking at scale", applied to a
variety of domains: text, graphs, relational data, etc. We will also
cover other components in the Hadoop ecosystem and alternative
programming models.</p>


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<a href="https://github.com/lintool/MapReduce-course-2013s" class="btn btn-primary btn-large">Fork me on Github!</a>
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