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Separating Hadoop Myths from Reality by ROB ANDERSON at Big Data Spain 2013

Cb6e6da05b5b943d2691ceefa3381cad?s=47 Big Data Spain
December 19, 2013

Separating Hadoop Myths from Reality by ROB ANDERSON at Big Data Spain 2013

According to Gartner, Hadoop is near the top of the Hype Cycle. While some customers have questions about the enterprise capabilities of Hadoop, the answers are clear as production deployments continue to expand. This session will use successful customer experiences to highlight the power of Hadoop and separate the myths from reality.


Big Data Spain

December 19, 2013


  1. Separating Hadoop Myths from Reality Rob Anderson

  2. 1   The  Myths  &  Reali.es   Surrounding  Hadoop  

      Rob  Anderson   VP  Systems  Engineering  
  3. 2   Sales   SCM   CRM   Public  

    Web  Logs   Produc7on   Data   Sensor     Data   Click   Streams   Loca7on   Social   Media   Billing   Enterprise   Data  Hub   Hadoop  Changes  Analy.cs   “Simple  algorithms  and  lots  of  data  trump   complex  models  ”   Halevy,    Norvig,  and    Pereira,  Google   IEEE  Intelligent  Systems    
  4. 3  

  5. 4  

  6. 5   Data   Warehouse   Volume   Variety  

  7. 6  

  8. 7   Big Data is hard to move…because it’s BIG

  9. 8   What  was  the  genius  of  Hadoop?   § 

    Fueling  an  industry  revolu7on   by  providing  infinite  capability   to  store  and  process  big  data   §  Expanding  analy7cs  across  data   types   §  Compelling  economics   –   20  to  100X  more  cost  effec7ve   than  alterna7ves  
  10. 9  

  11. 10   Random  Wri.ng  in  MapR   S1 S2 S3

    S5 S4 S1, S2, S4 S1, S3 S1, S4, S5 S2, S4, S5 S3 Client   wri.ng   data   CLDB   Ask  for   64M  block   Create  cont.   Picks  master   and  2  replica  slaves   Write   next  chunk   to  S2   S2, S3, S5 aZach  
  12. 11  

  13. 12   MapR   Spout   TwiZer   TwiZer  

       API   TwiZerLogger   Storm         MapR   Op7onal   MapReduce   DFS  
  14. 13   hZp://www.flickr.com/photos/onemoreshotrog/8085462024/  

  15. 14   Hadoop  Distribu.ons  

  16. Hadoop:  The  Disrup.ve  Technology     at  the  Core  of

     Big  Data  
  17. 16  

  18. 17   The  Reality  is     Architecture  MaHers  

  19. MapR  Data  System   Architecture  Comparison   HBase   JVM

      HDFS   JVM   ext3/ext4   Disks   Other  Distribu7ons   Disks   MapR  M7  
  20. Architecture  Results   Results  with  other   distribu.ons   Results

     with   MapR  M7  
  21. 20  

  22. Produc.on  Success  with  Hadoop  

  23. 22   2000+   Nodes   Fortune  100  Retailer  

  24. 23   1000+  Nodes   Fortune  100  Financial  Services  Company

  25. 24  

  26. 25   Produc7on  Hadoop  in     Waste  Management  

  27. 26   Suntory  whiskey  

  28. 27  

  29. 28   Unique  Iden.ty   Ini.a.ve,  India    

  30. None
  31. 30     Thank  you   Big  Data  Spain!  

  32. None