Opening Keynote at ZDNet Advanced Computing Conference by Abhishek Sinha (Business Development Manager APAC)

1. Cloud

2. What is big data
Data analysis Pipeline
How customers are using the pipeline

4. When your data sets become
so large that you have to start
innovating how to collect, store,
organize, analyze and share it

5. What does big data look like ?

6. Volume
Velocity
Variety
3Vs

7. Where is this data coming from ?

8. Human generated
Machine generated
Tweet
Surf the internet
Buy and sell products
Upload images and videos
Play games
Check in at restaurants
Search for cafes
Find deals
Watch content online
Look for directions
Use social media

9. Human generated
Machine generated
Networks and security
devices
Mobile phones
Cell phone towers
Smart grids
Smart meters
Telematics from cars
Sensors on machines
Videos from traffic and
security cameras

10. What is it used for ?

11. Data for competitive
advantage

12. Data for competitive
advantage
Customer Segmentation
Financial modeling,
System analysis,
Line-of-sight,
Replacing Human decisions
Business intelligence..

13. Data for competitive
advantage
Customer Segmentation
Financial modeling,
System analysis,
Line-of-sight,
Replacing Human decisions
Business intelligence..
Innovating new business and
revenue models

14. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation

15. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation
lower cost,
increased
throughput

16. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation
lower cost,
increased
throughput
constraint

17. Very high barrier to
turning data into
information…

18. Very high barrier to
turning data into
information.
Infrastructure capacity
Technical Skills
Questions to ask
Cheap experimentation

19. Amazon Web Services Cloud

20. Elastic and highly scalable
No upfront capital expense
Only pay for what you use
+
+
Available on-demand
+
=
Remove
constraints

21. Remove constraints = More experimentation
More experimentation = More innovation
More Innovation = Competitive edge

22. Amazon Web Services
Removes constraints
Focus on your data
Leave undifferentiated heavy lifting to us

23. HOW

24. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation

25. 25

26. AWS
Import/Export
Corporate
data center
Amazon
Elastic
MapReduce
Amazon
Simple
Storage
Service (S3)
BI Users
Clickstream data
from 500+
websites and VoD
platform

27. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation

28. More than 25 Million Streaming Members
50 Billion Events Per Day
30 Million plays every day
2 billion hours of video in 3
months
4 million ratings per day
3 million searches
Device location , time ,
day, week etc.
Social data

29. 10 TB of streaming data per day

30. What is S3?
Highly scalable data storage
Access via APIs
Fast
(850K requests
per sec)
Highly available & durable
(99.999999999% Durability
Economical
($0.095 per GB)*
Web store

31. Data consumed in multiple ways
S3
EMR
Prod Cluster
(EMR)
Recommen
dation
Engine
Ad-hoc
Analysis
Personalization

37. Velocity of data
Amazon Dynamodb

38. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation

39. “Who buys video games?”

40. 3.5 billion records
13 TB of click stream logs
71 million unique cookies
Per day:

42. 500% return on ad spend
17,000% reduction in
procurement time
Results:

43. “Who is using our
service?”

44. Identified early mobile usage
Invested heavily in mobile
development
Finding signal in the noise of logs

45. 9,432,061 unique mobile devices
used the Yelp mobile app.
4 million+ calls. 5 million+ directions.
In January 2013

49. What is EMR?
Map-Reduce engine Integrated with tools
Hadoop-as-a-service
Massively parallel
Cost effective AWS wrapper
Integrated to AWS servi

50. +
Source: http://nerds.airbnb.com/redshift-performance-cost
Table Size Query type Hive Redshift
3 billion
rows
Simple range
query
1680
seconds (28
min)
360 seconds
(6 min)
1 million
rows
2 complex
joins
182 seconds 8 seconds
$13.60/hour on Redshift versus $57/hour on
HIVE

51. Every day is crucial and costly

52. Challenge: To run a virtual screen with a higher
accuracy algorithm & 21 million compounds

54. Metric Count
Compute Hours of
Work
109,927 hours
Compute Days of
Work
4,580 days
Compute Years of
Work
12.55 years
Ligand Count ~21 million ligands
Using Cycle Computing and Amazon
Web Services

55. 3 Hours
for $4828.85/hr

56. Instead of $20+
Million in
Infrastructure

57. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation

58. Open web index.
3.4 billion records.
Available to all.
1000 Genomes
project

60. Generation
Collect
Store
Collaboration & sharing
Analysis and Computation

61. Thank you! aws.amazon.com/big-data
sinhaar@amazon.com
May 21st, COEX Auditorium, Seoul
One day Free training
Walk through of services
http://aws.amazon.com/apac/awsday/seoul/