2. D AV I D G E V O R K YA N
@ d a v i d g e v
d a v i d g e v o r k y a n

3. G R A D U AT E D A U A I N 2 0 0 8

4. W H AT I S B I G D ATA ?

5. FA S H I O N A B L E T E R M ?

6. 8 0 % O F D ATA E X I S T I N G I N A N Y E N T E R P R I S E I S
U N S T R U C T U R E D D ATA
ST R U C T U R E D
DATA
S E M I -­‐
ST R U C T U R E D
U N ST R U C T U R E D
DATA
RDBMS Data Warehousing

7. 9 0 % O F T H E D ATA I N T H E W O R L D T O D AY H A S
B E E N C R E AT E D I N T H E L A S T T W O Y E A R S A L O N E
S o u rc e : h t t p : / / w w w. i n t e l . c o m / c o n t e n t / w w w / u s / e n / c o m m u n i c a t i o n s / i n t e r n e t - m i n u t e - i n f o g r a p h i c . h t m l

8. 4 V ’ S O F B I G D ATA
VOLUME (large amount of data)
VARIETY (sensors, video, audio, email, social)
VELOCITY (speed of data generation)
VERACITY (authenticity and/or accuracy)

9. S O L U T I O N S R E Q U I R E D
f o rc e s y o u t o c h a n g e t h e w a y y o u
• C O L L E C T
• T R A N S P O RT
• S T O R E
• M A N A G E
• A N A LY Z E
• V I S U A L I Z E

11. W H AT I S D ATA S C I E N C E ?

12. D ATA S C I E N C E ! = S TAT I S T I C A L A N A LY S I S
I T I S S C I E N C E A N D “ A RT ” O F …
• E X P L O R I N G T H E U N K N O W N A B O U T D ATA
“ m a k e d i s c o v e r i e s w h i l e s w i m m i n g i n t h e d a t a ”
• R E F I N I N G T H E R E S U LT S F O R A C C U R A C Y
• D E R I V I N G A C T I O N A B L E I N S I G H T
• C R E AT I N G D ATA - D R I V E N P R O D U C T S

13. W H O A R E D ATA S C I E N T I S T S ?

14. W H O A R E D ATA S C I E N T I S T S ?
D re w C o n w a y, 2 0 1 0

15. B I G D ATA S C I E N C E T O O L S ?

16. • S c a l a , J a v a , P y t h o n , R … ( b o n u s : C l o j u re , H a s k e l l , E r l a n g )
• H a d o o p , H D F S , M a p R e d u c e … ( b o n u s : S p a r k , S t o r m , Te z )
• S c a l d i n g , H B a s e , P i g , H i v e … ( b o n u s : S h a r k , T i t a n , G i r a p h )
• F l u m e , S q o o p , E T L , We b s c r a p e r s … ( b o n u s : H u m e )
• S Q L , R D B M S , D W, O L A P… ( b o n u s : S O L R , E l a s t i c S e a rc h )
• K n i m e , We k a , R a p i d M i n e r… ( b o n u s : S c i P y, N u m P y, P a n d a s )
• D 3 . j s , K i b a n a , g g p l o t 2 , Ta b l e u … ( b o n u s : S h i n y, F l a re ,
D a t a m e e r )
• S P S S , M a t l a b , S A S … ( t h e e n t e r p r i s e m a n )
• N o S Q L , M o n g o D B , C a s s a n d r a , C o u c h D B
• A n d Ye s ! … M S - E x c e l : t h e m o s t u s e d , m o s t u n d e r r a t e d D S t o o l

18. G O A L ?

19. • R e v e n u e , re v e n u e , re v e n u e
• I m p ro v e t h e c u s t o m e r e x p e r i e n c e
• I n c re a s e o p e r a t i o n a l e ff i c i e n c y
• G E : O p t i m i z e m a i n t e n a n c e i n t e r v a l s f o r i n d u s t r i a l
p ro d u c t s
• G o o g l e : R e f i n e s e a rc h a n d a d - s e r v i n g a l g o r i t h m s
• Z y n g a : O p t i m i z e t h e g a m e e x p e r i e n c e f o r b o t h
l o n g - t e r m e n g a g e m e n t a n d re v e n u e
• N e t f l i x : M o v i e re c o m m e n d a t i o n s
• K a p l a n : U n c o v e r e ff e c t i v e l e a r n i n g s t r a t e g i e s
• e H a r m o n y : C re a t e h a p p y re l a t i o n s h i p s

20. W H O A R E W E ?

21. T R A D I T I O N A L M E T H O D S D O N O T W O R K
A N Y M O R E …

22. E H A R M O N Y C R E AT E S
T H E H A P P I E S T,
M O S T PA S S I O N AT E
A N D M O S T F U L F I L L I N G
R E L AT I O N S H I P S *
* A C C O R D I N G T O A R E C E N T S T U D Y

23. 4 3 8
M A R R I A G E S P E R D AY

24. T H E D I F F E R E N C E ?

25. T H E D I F F E R E N C E ?
Compatibility Matching System®
C O M PAT I B I L I T Y
M AT C H I N G
A F F I N I T Y
M AT C H I N G
M AT C H
D I S T R I B U T I O N

26. T H E D I F F E R E N C E ?
Compatibility Matching System®
C O M PAT I B I L I T Y
M AT C H I N G
A F F I N I T Y
M AT C H I N G
M AT C H
D I S T R I B U T I O N

27. U N I D I R E C T I O N A L U S E R D E F I N E D C R I T E R I A
Nicolette

28. U N I D I R E C T I O N A L U S E R D E F I N E D C R I T E R I A
B I D I R E C T I O N A L
Leo
Ian
Steve
Nicolette

29. U N I D I R E C T I O N A L U S E R D E F I N E D C R I T E R I A
Leo
Ian
Steve
Nicolette
B I D I R E C T I O N A L

33. 150
ques5ons
Personality
Values
A@ributes
Beliefs

34. Intellect
Energy
Sociability
Ambition
Kindness
Curiosity
Humor
Spirituality

35. C O M PAT I B I L I T Y M AT C H I N G
U S E R D E F I N E D
C R I T E R I A
C O M PAT I B I L I T Y
M O D E L S
M O N G O D B
V O L D E M O RT

36. M O N G O D B
DATA STORE NEEDS
P O W E R F U L
I N D E X I N G
M O D E L S
FA S T M U LT I -
AT T R I B U T E
S E A R C H E S
E A S Y T O
M A I N TA I N
6 0 M +
Q U E R I E S
per day

37. M O N G O D B
WINS
A U T O
S C A L I N G
B U I LT- I N
S H A R D I N G
A U T O
B A L A N C I N G
M M S

38. V O L D E M O RT ?
T H AT N A M E
S O U N D S FA M I L I A R

39. V O L D E M O RT
DATA STORE NEEDS
C R U D
O P E R AT I O N S
VA R I E D
T R A N S A C T I O N
S I Z E S
B I L L I O N +
P O T E N T I A L
M AT C H E S
per day

40. V O L D E M O RT
WINS
A U T O
R E P L I C AT I O N
A U T O
PA RT I T I O N I N G
P L U G G A B L E
S E R I A L I Z AT I O N

41. A F F I N I T Y M AT C H I N G
Compatibility Matching System®
C O M PAT I B I L I T Y
M AT C H I N G
A F F I N I T Y
M AT C H I N G
M AT C H
D I S T R I B U T I O N

42. 65 30
3000 miles

43. Commprobability
Distance in Miles
0 1 3 7 15 63 255 1023 4095
P R O B

45. Commprobability
Height difference in cm
-29 -25 -21 -17 -13 -9 -6 -3 0 3 6 9 12 16 20 24 28 32 36 40 44 48 52 56
4
-­‐
8
in
P R O B

46. W O R D S T O U S E

47. W O R D S T O U S E

48. S O M E I N S I G H T

49. D ATA N E E D S F O R A F F I N I T Y
5 0 M + R E G I S T E R E D U S E R S
1 0 3
AT T R I B U T E S
1 0 7
D A I LY M AT C H E S
2 5 0 M +
P H O T O S
4 B + Q U E S T I O N N A I R E S
A N S W E R E D

50. C O M M U N I C AT I O N A G G R E G AT E S
E V E N T L I S T E N E R
S E R V I C E
U S E R A C T I V I T Y
S E R V I C E
~ 5 M S
R E S P O N S E
T I M E S
1 0 K E V E N T S
P E R S E C O N D
U S E R
S E R V I C E
H O U R LY, D A I LY
T O TA L

51. O F F L I N E B AT C H J O B S
U S E R
S E R V I C E
M A P - S I D E J O I N S
( T B )
S C O R I N G
1+GB
Compressed
Protocol
Buffers
PA I R I N G S
S E R V I C E
750M
Compressed
Protocol
Buffers
B I L L I O N +
P O T E N T I A L
M AT C H E S

52. A M A Z O N
E M R
AW S D I R E C T
C O N N E C T
2 5 6 N O D E S
5 0 T B S T O R A G E
I N - H O U S E
S E A M I C R O
D ATA R E T R I E VA L L AT E N C Y
L O W O P E R AT I O N A L C O S T
L O W P O W E R C O N S U M P T I O N
P R E D I C TA B L E C O M P L E T I O N T I M E S

53. M O D E L R E T R A I N I N G
distcp
Protocol
Buffers
from
Offline
Jobs

54. M AT C H D I S T R I B U T I O N
Compatibility Matching System®
C O M PAT I B I L I T Y
M AT C H I N G
A F F I N I T Y
M AT C H I N G
M AT C H
D I S T R I B U T I O N

55. Delivering the right matches
at the right time to as many
people as possible across
the entire network

62. T H A N K Y O U
Q U E S T I O N S ?

63. C R E D I T S :
The Noun Project
http://thenounproject.com
Visual Elements From