"Big Data" is a commonly brandished term, but rarely does anyone provide a clear definition of it. If we stop to define Big Data, we find that at its core, it is about an inability to meet the challenges of information storage and access in a world where data is generated by the petabyte. As an alternative exists the concept of Critical Data, data that generates value for businesses, whose unavailability costs money or even lives. This is the data which has already been identified and understood to be at the core of a business. Together, we will gain a deeper understanding of how to gather, store, and access this business critical information.

1. THE BIG DATA CON
Why Big Data is a Problem Not a Solution
#TheBigDataCon

2. IAN PLOSKER
Director, Technical Operations, EMEA
Basho Technologies
@dstroyallmodels

3. WHO IS
basho
?

4. WE MAKE

6. DISCLAIMERS

7. ALL OPINIONS EXPRESSED HEREIN
ARE MY OWN AND NOT THOSE OF
MY EMPLOYER OR ANYONE ELSE

8. I’M NOT TROLLING

9. I’M NOT FUD RAKING

10. WELL, MAYBE JUST A LITTLE
BIT

11. LET’S GET STARTED

12. WHAT IS BIG DATA?

13. HOW BIG IS BIG?

14. GIGABYTES, TERABYTES,
PETABYTES, EXABYTES?

15. IF THERE’S BIG DATA,
SHOULDN’T THERE ALSO BE
MEDIUM AND SMALL DATA

16. BIG DATA IS MORE DATA
THAN YOU KNOW WHAT TO
DO WITH

17. BIG DATA IS THE DATA THAT
YOU DON’T KNOW WHAT TO
DO WITH

18. THE PROMISE OF BIG DATA

19. IS TO STORE THE REAMS

20. YOU DON’T KNOW HOW TO
USE

21. SO YOU CAN EXTRACT
VALUE FROM IT IN THE
FUTURE

22. LET’S BE HONEST

23. THE PEOPLE WHO ARE
MAKING MONEY OFF OF BIG
DATA

24. ARE THE PEOPLE
EXTRACTING VALUE FROM IT
TODAY

25. I.E. THE PEOPLE SELLING BIG
DATA SOLUTIONS

26. EVERYONE AND THEIR
GRANDMA ARE TRYING TO
GET IN ON THE ACTION

27. VENDORS ARE REPACKAGING
THE SAME OLD THING

28. AND TRYING TO TRICK US
INTO THINKING ITS THE NEW
HOTNESS

29. LET’S NOT BE FOOLED BY
MARKETING

30. WORDS ARE IMPORTANT

31. MARKETING WORKS TO
SEPARATE WORDS FROM
THEIR MEANING

32. FROM THEIR ORIGINAL
INTENT

33. TO GET YOU TO ASSOCIATE
WORDS

34. WITH PARTICULAR

35. PRODUCTS

36. BRANDS

37. AND VENDORS

38. </RANT>

39. LET’S TRY TO IMPROVE THE
STATE OF DISCOURSE

40. SO LET’S BRING SOME NEW
CATCHPHRASES TO THE TABLE

41. SO WE DON’T HAVE TO TALK
ABOUT BIG DATA ANYMORE

42. INSTEAD LET’S TALK ABOUT
CRITICAL DATA

43. WHAT IS CRITICAL DATA?

44. Is your data really
that critical,
dude?

45. IT’S MISSION CRITICAL DATA

46. IT’S DATA WHOSE
UNAVAILABILITY

47. COSTS YOU MONEY

48. CRED

49. OR LIVES

50. IT IS THE DATA NEEDED NOW

51. NOT AT SOME DISTANT
POINT IN THE FUTURE

52. IT IS THE DATA THAT YOU

53. YOUR CUSTOMERS

54. OR SOCIETY

55. CAN CAPITALIZE ON TODAY

56. ITS VALUE IS CAPTURED BY
THE OWNERS OF THE DATA

57. RATHER THAN A THIRD PARTY

58. HOW DO YOU IDENTIFY
YOUR CRITICAL DATA?

59. LATENCY AT SOME MARGIN
APPEARS SIMPLY AS
UNAVAILABILITY

60. Who cares about latency?
Sometimes high latency looks like an outage
to the end user.

61. FOR AMAZON:
100MS LATENCY
DECREASES SALES BY 1%
Source: http://sites.google.com/site/glinden/Home/StanfordDataMining.2006-11-28.ppt

62. FOR GOOGLE:
A 500MS INCREASE IN LATENCY
REDUCED TRAFFIC
BY 20 PERCENT
Source: http://sites.google.com/site/glinden/Home/StanfordDataMining.2006-11-28.ppt

63. WHAT DOES A SYSTEM FOR
CRITICAL DATA LOOK LIKE?

64. STREAMING PROCESSING

65. STORM

66. RIAK_PIPE

68. EXAMPLES OF DYNAMO
SYSTEMS

70. VOLDEMORT

72. THESE SYSTEMS SACRIFICE
CONSISTENCY FOR HIGH
AVAILABILITY/LOW LATENCY

73. I'M NOT MAKING A SALES
PITCH

74. DON'T USE MY DATABASE

75. SERIOUSLY, DON'T

76. UNLESS

77. YOU ARE WILLING TO
SACRIFICE

78. FAMILIAR DATA AND QUERY
MODELS

79. FAMILIAR HIRING PATTERNS

80. KNOWN OPERATIONAL
ISSUES

81. FOR

82. PREDICTABLE LATENCY

83. AVAILABILITY

84. PREDICTABLE OPERATIONS

85. OR IF DATA UNAVAILABILITY
COSTS YOU $$$ OR MORE

86. THANKS

87. ian@basho.com
@dstroyallmodels
github.com/ian-plosker