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

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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.

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

  1. 1. Separating Hadoop Myths from Reality Rob Anderson
  2. 2. The  Myths  &  Reali.es   Surrounding  Hadoop     Rob  Anderson   VP  Systems  Engineering   1  
  3. 3. Hadoop  Changes  Analy.cs   “Simple  algorithms  and  lots  of  data  trump   complex  models  ”   Halevy,    Norvig,  and    Pereira,  Google   IEEE  Intelligent  Systems     Loca7on   Click   Streams   Sensor     Data   Enterprise   Data  Hub   Produc7on   Data   Web  Logs   Public   Social   Media   Sales   SCM   CRM   2   Billing  
  4. 4. 3  
  5. 5. 4  
  6. 6. Variety   Volume   Velocity   Data   Warehouse   5  
  7. 7. 6  
  8. 8. Big Data is hard to move…because it’s 7   BIG
  9. 9. 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   8  
  10. 10. 9  
  11. 11. Random  Wri.ng  in  MapR   Client   wri.ng   data   Write   next  chunk   to  S2   S1 Ask  for   64M  block   Create  cont.   aZach   CLDB   S1, S2, S4 S1, S3 S1, S4, S5 S2 Picks  master   and  2  replica  slaves   S2, S4, S5 S3 S2, S3, S5 S4 S3 10   S5
  12. 12. 11  
  13. 13. TwiZer   TwiZer      API   MapR   Spout   DFS   TwiZerLogger   MapR   Op7onal   MapReduce   Storm         12  
  14. 14. hZp://www.flickr.com/photos/onemoreshotrog/8085462024/   13  
  15. 15. Hadoop  Distribu.ons   14  
  16. 16. Hadoop:  The  Disrup.ve  Technology     at  the  Core  of  Big  Data  
  17. 17. 16  
  18. 18. The  Reality  is     Architecture  MaHers   17  
  19. 19. Architecture  Comparison   HBase   JVM   HDFS   JVM   ext3/ext4   MapR  Data  System   Disks   Disks   Other  Distribu7ons   MapR  M7  
  20. 20. Architecture  Results   Results  with  other   distribu.ons   Results  with   MapR  M7  
  21. 21. 20  
  22. 22. Produc.on  Success  with  Hadoop  
  23. 23. 2000+   Nodes   Fortune  100  Retailer   22  
  24. 24. 1000+  Nodes   Fortune  100  Financial  Services  Company   23  
  25. 25. 24  
  26. 26. Produc7on  Hadoop  in     Waste  Management   25  
  27. 27. Suntory  whiskey   26  
  28. 28. 27  
  29. 29. Unique  Iden.ty   Ini.a.ve,  India     28  
  30. 30.   Thank  you   Big  Data  Spain!   30  

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