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Cloud
The big data pipeline
How customers are using the pipeline
The big data eco-system on the cloud
Generation
Collect
Store
Collaboration & sharing
Analysis and Computation
Generation
Collect
Store
Collaboration & sharing
Analysis and Computation
lower cost,
increased
throughput
Generation
Collect
Store
Collaboration & sharing
Analysis and Computation
lower cost,
increased
throughput
constraint
Generated data
Available for analysis
Data volume
Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011
IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares
Very high barrier to
turning data into
information…
Very high barrier to
turning data into
information.
Infrastructure capacity
Technical Skills
Questions to ask
Cheap experimentation
Amazon Web Services Cloud
Elastic and highly scalable
No upfront capital expense
Only pay for what you use
+
+
Available on-demand
+
=
Remove
constraints
Remove constraints = More experimentation
More experimentation = More innovation
More Innovation = Competitive edge
Amazon Web Services
Removes constraints
Focus on your data
Leave undifferentiated heavy lifting to us
big data
Bankinter uses HPC on AWS for Monte Carlo
Simulation
“Bankinter uses AWS as an
integral part of our credit-
risk simulation application;
We need to perform at
least 5,000,000 simulations
to get realistic results”
Credit
Data
Average simulation
time went from 23 hours to 20 minutes
Challenge:
Learn about customer based on
what they do, rather than what
they say (i.e., data exhaust);
virtually unlimited data
Solution:
Always-on cluster continually
processes new financial data
and stores results in S3.
Collaborative filtering used to
provide recommendations and
ad-hoc queries performed
using Hive.
For illustrative purposes only.
S&P Capital IQ
Microsoft
SQL Server
Amazon S3:
• Companies You May
Be Interested In
Amazon S3:
• Clicks
• Key Developments
• Company Profiles
Amazon Elastic Map-Reduce:
• Compute User Selectivity
• Compute Key Developments
• Join & Score
Challenge:
Volatile weather is deadly to crops like grapes and tomatoes
Solution:
Built a predictive model based on freely available data—60 years of
crop data, 14 TBs of soil data, and one million government Doppler
radar points. 50 hadoop clusters process new data as it comes into S3
each day, continuously updating the model.
150B Soil
Observations
3M Daily
Weather
Measurements
850K Precision
Rainfall Grids
Tracked
Simulations Each Month
• Per Simulation:
• 10K Unique Scenarios Generated
• 5 Trillion Datapoints
• 5-6k Node Hadoop Cluster
AWS
Import/Export
Corporate
data center
Amazon
Elastic
MapReduce
Amazon
Simple
Storage
Service (S3)
BI Users
Clickstream data
from 500+
websites and VoD
platform
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
10 TB of streaming data per day
Data consumed in multiple ways
S3
EMR
Prod Cluster
(EMR)
Recommen
dation
Engine
Ad-hoc
Analysis
Personalization
Amazon Dynamodb
“Who buys video games?”
3.5 billion records
13 TB of click stream logs
71 million unique cookies
Per day:
500% return on ad spend
17,000% reduction in
procurement time
Results:
“Who is using our
service?”
Identified early mobile usage
Invested heavily in mobile
development
Finding signal in the noise of logs
9,432,061 unique mobile devices
used the Yelp mobile app.
Every day is crucial and costly
Challenge: To run a virtual screen with a higher
accuracy algorithm & 21 million compounds
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
3 Hours
for $4828.85/hr
Relational Database Service
Fully managed database
(MySQL, Oracle, MSSQL)
DynamoDB
NoSQL, Schemaless,
Provisioned throughput
database
S3
Object datastore up to 5TB
per object
99.999999999% durability
Map-Reduce engine
Hadoop-as-a-service
Massively parallel
Cost effective AWS wrapper
Amazon Elastic MapReduce
Amazon
Redshift
data warehouse service
petabyte-scale
fast and fully managed
RDBMS
Redshift
OLTP
ERP
Reporting
and BI
+
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
Generation
Collect
Store
Collaboration & sharing
Analysis and Computation
Thank you! aws.amazon.com/big-data
May 14st, Kowloonbay International Trade
& Exhibition Centre (KITEC), Hong Kong
One day Free training
Walk through of services
http://aws.amazon.com/apac/awsday/hk/

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