Big Data Revolution: Benefit from MapReduce Without the Risk

Ted Dunning (MapR Technologies)
Sponsored Sessions
Location: Murray Hill Suite B
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Map-reduce and Hadoop provide new scaling opportunities for analyzing data. As a result organizations are beginning to analyze and derive business value from large amounts of data that, in many cases, were previously simply being discarded. In some cases such as on-line advertising, the ability to analyze these previously impenetrable volumes of data have disrupted entire industries such as is the case with on-line advertising.

Such green field opportunities are rare, however, and few companies can afford to build an entirely new analytics pipeline. Integrating big data analytics systems like Apache Hadoop into existing analytics systems can be very difficult, however, because there are huge differences in the fundamental approaches being taken to the basic problems of how data should be accessed and analyzed.

These differences are exactly what makes these new technologies hugely effective, but they are also what makes integration between conventional and new approaches so difficult.

This talk will provide detailed descriptions of how to use new technologies to

  • Get data into and out of the Hadoop cluster as quickly as possible
  • Allow real-time components to easily access cluster data
  • Use well-known and understood standard tools to access cluster data
  • Make Hadoop easier to use and operate
  • Capitalize on existing code in map-reduce settings
  • Integrate map-reduce systems into existing analytic systems

These descriptions will be taken from real-life customer situations. Each will describe the problems faced and the solutions that solved these problems.

This session is sponsored by MapR Technologies

Photo of Ted Dunning

Ted Dunning

MapR Technologies

Ted Dunning has been involved with a number of startups—the latest is MapR Technologies, where he is chief application architect working on advanced Hadoop-related technologies. Ted is also a PMC member for the Apache Zookeeper and Mahout projects and contributed to the Mahout clustering, classification, and matrix decomposition algorithms. He was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics. Opinionated about software and data-mining and passionate about open source, he is an active participant of Hadoop and related communities and loves helping projects get going with new technologies.


  • Aster Data
  • EMC Greenplum
  • GE
  • Lexis Nexis
  • MarkLogic
  • Tableau Software
  • Cloudera
  • DataStax
  • Informatica
  • DataSift
  • Splunk
  • Amazon Web Services
  • Datameer
  • Impetus
  • Karmasphere
  • MapR Technologies
  • Pervasive
  • Platform Computing
  • Revolution Analytics
  • Sybase
  • Xeround
  • Media-Science
  • Platfora

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