David Gevorkyan graduated from AUA in 2008. He discusses big data, data science, and how eHarmony uses these fields to create successful relationships. eHarmony collects vast amounts of user data through detailed questionnaires and analyzes this data using algorithms to make compatibility match recommendations to users.

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