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<speaker>
  <biography></biography>
  <company>Ambient Ideas, LLC</company>
  <company-website>http://www.ambientideas.com</company-website>
  <id type="integer">3883</id>
  <linkedin-url>http://www.linkedin.com/in/matthewatambientideas</linkedin-url>
  <location>Denver, Colorado, USA</location>
  <name>Matthew McCullough</name>
  <personal-website>http://ambientideas.com/blog</personal-website>
  <title>Managing Partner, Open Source Architect</title>
  <twitter-username>matthewmccull</twitter-username>
  <average-rating type="decimal">4.54</average-rating>
  <avatar-url>/avatars/3883/thumb/stream.18325.0</avatar-url>
  <talks type="array">
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      <created-at type="datetime">2010-01-10T19:11:36+00:00</created-at>
      <event-id type="integer">284</event-id>
      <id type="integer">1941</id>
      <info>Moore&#8217;s law has finally hit the wall and CPU speeds have actually decreased in the last few years. The industry is reacting with hardware with an ever-growing number of cores and software that can leverage &#8220;grids&#8221; of distributed, often commodity, computing resources. But how is a traditional Java developer supposed to easily take advantage of this revolution? The answer is the Apache Hadoop family of projects. Hadoop is a suite of Open Source APIs at the forefront of this grid computing revolution and is considered the absolute gold standard for the divide-and-conquer model of distributed problem crunching. The well-travelled Apache Hadoop framework is curently being leveraged in production by prominent names such as Yahoo, IBM, Amazon, Adobe, AOL, Facebook and Hulu just to name a few.

Details

In this session, you&#8217;ll start by learning the vocabulary unique to the distributed computing space. Next, we&#8217;ll discover how to shape a problem and processing to fit the Hadoop MapReduce framework. We&#8217;ll then examine the incredible auto-replicating, redundant and self-healing HDFS filesystem. Finally, we&#8217;ll fire up several Hadoop nodes and watch our calculation process get devoured live by our Hadoop grid. At this talk&#8217;s conclusion, you&#8217;ll feel equipped to take on any massive data set and processing your employer can throw at you with absolute ease.</info>
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      <slides-url>http://ambientideas.com/blog</slides-url>
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      <talk-url>http://denverjug.wordpress.com/2010/01/08/djug-meeting-113-matthew-mccullough-hadoop-encryption-boot-camp-on-the-jvm/</talk-url>
      <title>Hadoop: Divide and Conquer Gigantic Datasets</title>
      <updated-at type="datetime">2010-01-10T19:11:36+00:00</updated-at>
      <when type="datetime">2010-01-13T19:00:00+00:00</when>
      <average-rating type="decimal">4.66</average-rating>
    </talk>
    <talk>
      <created-at type="datetime">2010-01-10T19:09:24+00:00</created-at>
      <event-id type="integer">284</event-id>
      <id type="integer">1940</id>
      <info>Does your application transmit customer information?  Are there fields of sensitive customer data stored in your DB?  Can your application be used on insecure networks?  If so, you need a working knowledge of encryption and how to leverage Open Source APIs and libraries to make securing your data as easy as possible.  Encryption is quickly becoming a developer&#8217;s new frontier of responsibility in many data-centric applications.

In today&#8217;s data-sensitive and news-sensationalizing world, don&#8217;t become the next headline by an inadvertent release of private customer or company data.  Secure your persisted, transmitted and in-memory data and learn the terminology you&#8217;ll need to navigate the ecosystem of symmetric and public/private key encryption.</info>
      <location nil="true"></location>
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      <slides-url>http://ambientideas.com/blog</slides-url>
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      <talk-url>http://denverjug.wordpress.com/2010/01/08/djug-meeting-113-matthew-mccullough-hadoop-encryption-boot-camp-on-the-jvm/</talk-url>
      <title>Encryption Boot Camp on the JVM</title>
      <updated-at type="datetime">2010-01-10T19:09:24+00:00</updated-at>
      <when type="datetime">2010-01-13T18:00:00+00:00</when>
      <average-rating type="decimal">4.41</average-rating>
    </talk>
  </talks>
</speaker>
