There are a number of assumptions that come with using standard Hadoop that are based on Hadoop's initial architecture. Many of these assumptions can be relaxed with more advanced architectures such as those provided by MapR. These changes in assumptions have ripple effects throughout the system architecture. This is significant because many systems like Mahout provide multiple implementations of various algorithms with very different performance and scaling implications.
I will describe several case studies and use these examples to show how these changes can simplify systems or, in some cases, make certain classes of programs run an order of magnitude faster.
About the speaker: Ted Dunning - Chief Application Architect (MapR)
Ted has held Chief Scientist positions at Veoh Networks, ID Analytics and at MusicMatch, (now Yahoo Music). Ted is responsible for building the most advanced identity theft detection system on the planet, as well as one of the largest peer-assisted video distribution systems and ground-breaking music and video recommendations systems. Ted has 15 issued and 15 pending patents and contributes to several Apache open source projects including Hadoop, Zookeeper and Hbase. He is also a committer for Apache Mahout. Ted earned a BS degree in electrical engineering from the University of Colorado; a MS degree in computer science from New Mexico State University; and a Ph.D. in computing science from Sheffield University in the United Kingdom. Ted also bought the drinks at one of the very first Hadoop User Group meetings.