Morning with MongoDB Paris 2012 - Making Big Data Small

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Matt Asay, VP Strategy, 10gen (the MongoDB company)

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Morning with MongoDB Paris 2012 - Making Big Data Small

  1. 1. Making Big Data Small: Big Data for the Rest of Us November 2012 1
  2. 2. …2000•Google announced it had released the largestsearch engine on the Internet•Google’s new index comprised more than 1 billionURLs•BIG!!! 2
  3. 3. …2008•Our indexing system for processing links indicatesthat we now count 1 trillion unique URLs(and the number of individual web pages out thereis growing by several billion pages per day)•BIGGER!!!! 3
  4. 4. An unprecedentedamount of data is beingcreated and is accessible 4
  5. 5. 5
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  7. 7. • Applies to more than just CPUs• Summary version? Things double at regular intervals• It’s exponential growth…and applies to Big DataBBC: “Your current PC is more powerful than the computer they had on board the first flight to the moon.” 7
  8. 8. 9,00090006750 4,4004500 2,1502250 1,000 500 55 120 250 1 4 10 24 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 8
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  12. 12. Source: Silicon Angle, 2012 12
  13. 13. Source: Silicon Angle, 2012 13
  14. 14. Source: Silicon Angle, 2012 14
  15. 15. Storage/OperationsProcessing/Analytics 15
  16. 16. 16
  17. 17. • In 1998 Google won the search race through custom software and infrastructure• In 2002 Amazon again wrote custom and proprietary software to handle their BIG Data needs• In 2006 Facebook started with off the shelf software, but quickly turned to developing their own custom-built solutionsWhat do these have in common? Big Data was critical to making them win 17
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  20. 20. • MS Office -> OpenOffice• Oracle DB -> PostgreSQL• Unix -> Linux• Weblogic -> JBoss• Documentum -> Alfresco• Cognos -> Pentaho/Jasper• Salesforce ->SugarCRM• Informatica -> Talend• iOS -> Android (?)• Etc. 20
  21. 21. Web Innovation OSS companies Vendor-sponsoredIndividual developers 21
  22. 22. 22
  23. 23. “The best minds of my generation are thinking about how to make people click ads.” (Jeff Hammerbacher)23 23
  24. 24. Where Do We Go from Here? 24
  25. 25. Agile Development • Iterative & continuous • New and emerging appsVolume and Typeof Data• Trillions of records• 10’s of millions of New Architectures queries per second • Systems scaling horizontally,• Volume of data not vertically• Semi-structured and • Commodity servers unstructured data • Cloud Computing 25
  26. 26. stormApache Drill 26
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  28. 28. World’s Most Popular Big Data Sources, 2012 Source: JasperSoft, 2012 28
  29. 29. The future is hu MONGOus 29
  30. 30. 5,900 companies evaluated. 10gen is #1 in Software and #9 overall 30
  31. 31. “Relational databases…[don’t] necessarily match the way we see ourdata. mongoDB gave us the flexibility to store data in the way thatwe understand it as opposed to somebody’s theoretical view.” “It’s friendly. By friendly, I mean that coming from a relational background, specifically a MySQL background, a lot of the concepts carry over.... It makes it very easy to get started.”“Selecting MongoDB as our database platform was a no brainer as thetechnology offered us the flexibility and scalability that we knewwe’d need for Priority Moments.” 31
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  37. 37. @mjasay 37

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