ANALYZING LARGE-SCALE USER DATA from Structure:Data 2012

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Presentation from Aaron Kimball, WibiData #dataconf More at http://event.gigaom.com/structuredata/

Technologie

ANALYZING LARGE-SCALE USER DATA

SPEAKER: Aaron Kimball
CTO
WibiData

Friday, July 27, 2012

Analyzing
Large-‐Scale
User
Data
with
Hadoop
and
HBase

Aaron
Kimball
–
CTO

WibiData,

Friday, July 27, 2012

We
can
now
collect

more
data
than
at

any
Dme
in
history.

Friday, July 27, 2012

Yesterday’s
engineering

challenge:
FiJng
the

problem
into
the

hardware.
Friday, July 27, 2012

Today’s
constrained

resource
is

understanding.

Friday, July 27, 2012

How
do
we
best
apply

data

…to
beMer
serving
our

Friday, July 27, 2012

The
best
products
are
user-‐
• IntuiDve
UI
• ConDnuously
learning
– Guided
search
– Smarter
recommenda1ons
• More
eﬀec1ve
service

Friday, July 27, 2012

What
are
we
building

Friday, July 27, 2012

Requirements

1.
Understand
the
user

populaDon
Friday, July 27, 2012

Requirements

2.
Respond
to

users
in
real

Dme

Friday, July 27, 2012

Requirements

3.
Support
graceful
data

evoluDon
Friday, July 27, 2012

Large-‐scale
data
science
is

• What
does
a
user
look
like?
– What
data
is
available
about
the
user?
– Which
features
are
important?
– Which
features
are
correlated?
• How
do
I
model
this
in
MapReduce?
• How
do
I
serve
results
in
a
Dmely

Friday, July 27, 2012

Tools
of
the
trade
• Store
all
data
about
a

user
in
one
place
• Support
real-‐Dme

get/put,
as
well
as

MapReduce

Friday, July 27, 2012

Tools
of
the
trade
• Use
complex
data

types
to
model

complex
data
• Support
extended

data
models
over

Dme

Friday, July 27, 2012

Tools
of
the
trade
• Abstract
computaDonal

model
away
from

MapReduce
• Support
computaDon

over
all
users…
or
one

user
at
a
Dme

Friday, July 27, 2012

:
for
set-‐top
boxes

Viewing/recording

history

Friday, July 27, 2012

:
for
set-‐top
boxes

Libraries
Device
and
User
Analysis

Viewing/recording

history

Personalized
oﬀers

and

recommenda=ons

Friday, July 27, 2012

:
for
set-‐top
boxes

Libraries
Device
and
User
Analysis

Viewing/recording

history

Personalized
oﬀers

and

recommenda=ons

Analysis
for

product

roadmap
Friday, July 27, 2012

:
for
set-‐top
boxes

Libraries
Device
and
User
Analysis

Viewing/recording

history

Personalized
oﬀers

and

recommenda=ons

Analysis
for

product
Tech
support

roadmap portal
Friday, July 27, 2012

:
for
set-‐top
boxes

Libraries
Device
and
User
Analysis

Viewing/recording

history

Personalized
oﬀers

and

recommenda=ons

Improve
Analysis
for

d
reports

product
Tech
support

for

roadmap portal
Friday, July 27, 2012 adver=se

The
future
• More
personalizaDon
• AdapDve
UIs
(self
arranging

dashboards)
• Targeted
content,
ads
• More
eﬀecDve
customer
service

Friday, July 27, 2012

Conclusions
• ApplicaDons
are
becoming

increasingly

user-‐centric
• Data
drives
this
capability,
but

harnessing
it
requires
a
new

distributed
architecture

Friday, July 27, 2012

www.wibidata.com
/

Aaron
Kimball
–
aaron@wibidata.com

Friday, July 27, 2012

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ANALYZING LARGE-SCALE USER DATA from Structure:Data 2012

1. ANALYZING LARGE-SCALE USER DATA SPEAKER: Aaron Kimball CTO WibiData Friday, July 27, 2012

2. Friday, July 27, 2012

3. Analyzing Large-‐Scale User Data with Hadoop and HBase Aaron Kimball – CTO WibiData, Friday, July 27, 2012

4. We can now collect more data than at any Dme in history. Friday, July 27, 2012

5. Yesterday’s engineering challenge: FiJng the problem into the hardware. Friday, July 27, 2012

6. Today’s constrained resource is understanding. Friday, July 27, 2012

7. How do we best apply data …to beMer serving our Friday, July 27, 2012

8. The best products are user-‐ • IntuiDve UI • ConDnuously learning – Guided search – Smarter recommenda1ons • More eﬀec1ve service Friday, July 27, 2012

9. What are we building Friday, July 27, 2012

10. What are we building Friday, July 27, 2012

11. What are we building Friday, July 27, 2012

12. What are we building Friday, July 27, 2012

13. What are we building Friday, July 27, 2012

14. What are we building Friday, July 27, 2012

15. What are we building Friday, July 27, 2012

16. What are we building Friday, July 27, 2012

17. What are we building Friday, July 27, 2012

18. What are we building Friday, July 27, 2012

19. Requirements 1. Understand the user populaDon Friday, July 27, 2012

20. Requirements 2. Respond to users in real Dme Friday, July 27, 2012

21. Requirements 3. Support graceful data evoluDon Friday, July 27, 2012

22. Large-‐scale data science is • What does a user look like? – What data is available about the user? – Which features are important? – Which features are correlated? • How do I model this in MapReduce? • How do I serve results in a Dmely Friday, July 27, 2012

23. Friday, July 27, 2012

24. Tools of the trade • Store all data about a user in one place • Support real-‐Dme get/put, as well as MapReduce Friday, July 27, 2012

25. Tools of the trade • Use complex data types to model complex data • Support extended data models over Dme Friday, July 27, 2012

26. Tools of the trade • Abstract computaDonal model away from MapReduce • Support computaDon over all users… or one user at a Dme Friday, July 27, 2012

27. Friday, July 27, 2012

28. Friday, July 27, 2012

29. Friday, July 27, 2012

30. Friday, July 27, 2012

31. Friday, July 27, 2012

32. Friday, July 27, 2012

33. Friday, July 27, 2012

34. : for set-‐top boxes Viewing/recording history Friday, July 27, 2012

35. : for set-‐top boxes Viewing/recording history Friday, July 27, 2012

36. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Friday, July 27, 2012

37. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Friday, July 27, 2012

38. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Analysis for product roadmap Friday, July 27, 2012

39. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Analysis for product roadmap Friday, July 27, 2012

40. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Analysis for product Tech support roadmap portal Friday, July 27, 2012

41. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Analysis for product Tech support roadmap portal Friday, July 27, 2012

42. : for set-‐top boxes Libraries Device and User Analysis Viewing/recording history Personalized oﬀers and recommenda=ons Improve Analysis for d reports product Tech support for roadmap portal Friday, July 27, 2012 adver=se

43. The future • More personalizaDon • AdapDve UIs (self arranging dashboards) • Targeted content, ads • More eﬀecDve customer service Friday, July 27, 2012

44. Conclusions • ApplicaDons are becoming increasingly user-‐centric • Data drives this capability, but harnessing it requires a new distributed architecture Friday, July 27, 2012

45. www.wibidata.com / Aaron Kimball – aaron@wibidata.com Friday, July 27, 2012

46. Friday, July 27, 2012

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