This webcast is the fourth in a series on why I/O is strategic for the data center. John Webster, senior partner at the Evaluator Group, will discuss why I/O is critically important to meet the bandwidth demands of big data deployments. As the data center infrastructure scales upward, so will the need for I/O to scale dynamically to meet these needs.

1. Why I/O Is Strategic
for Big Data
Presented by: Emulex and
Evaluator Group
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2. Webcast Housekeeping
1. All attendees will be on mute during the presentation
2. Please submit your questions via the text/chat feature
3. We will do all Q&A at the end of the presentation
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3. Why I/O Is Strategic
Katherine Lane
Director of Corporate
Communications
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4. Why I/O Is Strategic?
Building a Virtual Panel of Experts!
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5. Topics for the Virtual Panel
Server Cloud Big Network
Virtualization Computing Data Convergence
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6. Moving the Elephant
Through the Pipes
John Webster
Senior Partner
Evaluator Group
© 2012 Evaluator Group, Inc.

7. Overview
 “Big data” can mean two different things
— Storage for large amounts of data
— Analytics against very large amounts of data
— I/O is critical for both
 Big Data Apps
— Personalized Healthcare
— Online-style shopping for bricks-and-mortar retailers
— Fraud detection
 Marketing Needs it Now
— Correlate customer data with social media data feeds
— Understand the buyer as an individual
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© 2012 Evaluator Group, Inc.

8. Customer
Data Analytics Model for Individualized Marketing
Profiles
NoSQL DB High Scale Data
HDFS BI and
Reductions
Analytics
Logs, Predictions
Tweets
Location on Buying
Behavior
4) Real-time:
Expert System Determine Best
Offer For This
Low 3) Input Into
Customer
Latency
1) Identify
2b) Lookup NoSQL DB User
Location
2a)Lookup
User Profile
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© 2012 Evaluator Group, Inc.

9. Distributed, Shared-Nothing
Architectures for Big Data Analytics
1 2 3 4 5 6 7 8 Console
Network
Link Link Link Link Pwr
B8GMR3
Active Active Active Active Active
Layer
C N N N N
O O O O O
N
D D D D
Compute T
E E E E
R
Layer O
L 1 2 3 n
Storage
Layer
DAS DAS DAS DAS DAS
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© 2012 Evaluator Group, Inc.

10. CAP theorem
It is impossible for a distributed computer system
to simultaneously provide all three of the
following guarantees:
 Consistency (all nodes see the same data at the same
time)
 Availability (a guarantee that every request receives a
response about whether it was successful or failed)
 Partition tolerance (the system continues to operate
despite arbitrary message loss or failure of part of the
system)
A distributed system can satisfy any two of these
guarantees at the same time, but not all three
© 2012 Evaluator Group, Inc.

11. The Impact of Network and I/O Performance
 The impacts of internal analytics system
network performance—both positive and
negative—are experienced at the level of
analytics application users.
 The rate at which data flows between storage
and processors within a Hadoop cluster has a
direct effect on cluster performance and
scalability.
 Getting data into and out of distributed
computing clusters impacts how quickly query
results are delivered to users.
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© 2012 Evaluator Group, Inc.

12. Internal Network Throughput 1GbE
© 2012 Evaluator Group, Inc.

13. Internal Network Throughput 10GbE
© 2012 Evaluator Group, Inc.

14. Load/Unload Throughput
© 2012 Evaluator Group, Inc.

15. Why Enterprise IT is Now Involved
 Distributed computing for analytics (Hadoop, for example) is
moving from science experiment to mission-critical
 Emerging Enterprise Hadoop use cases include:
— Hadoop for very large data sets that can’t be analyzed economically
by the data warehouse
— Hadoop on the front-end of the data warehouse
— Hadoop as data convergence engine – combine new unstructured
data sources with structured data warehouse data
— Hadoop as the back-end to the data warehouse
 Also emerging in the need to bring Hadoop under the data
governance umbrella
— Use case for NAS/SAN attached to Hadoop clusters?
— At what cost?
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© 2012 Evaluator Group, Inc.

16. Is Hadoop Ready for Prime Time?
 Hadoop was not born and raised in the highly
risk averse, enterprise data center
 Hadoop puts forward a different and
inefficient operational model from the
standpoint of enterprise IT
 Hadoop introduces enterprise security and
data governance issues
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© 2012 Evaluator Group, Inc.

17. Shared Storage as Secondary Storage
Network 1 Link 2 3 Link 4 5 Link 6 7 Link 8 Pwr Console
B8GMR3
Active Active Active Active Active
Layer
C N N N N
O
N O O O O
Compute T
R D D D D
Layer O
L
E E E E
1 2 3 n
Storage
Layer
SAN/NAS
© 2012 Evaluator Group, Inc.

18. Shared Storage as Primary Storage
Network 1 Link 2 3 Link 4 5 Link 6 7 Link 8 Pwr Console
B8GMR3
Active Active Active Active Active
Layer
C N N N N
O
N O O O O
Compute T
R D D D D
Layer O
L
E E E E
1 2 3 n
Storage SAN and Scale-out NAS
Layer
© 2012 Evaluator Group, Inc.

19. Evaluating Hadoop as a Storage Device
 Single Points of Failure Eliminated?
 SSD and automated tiering?
 Dedupe?
 Snapshots?
 Insert your hot-button storage feature here:
__________
© 2012 Evaluator Group, Inc.

20. Enterprise IT and Big Data Analytics
 There will be Big Data—Storage and Apps
 The traditional data warehouse will continue to evolve
 Distributed computing clusters (XxSQL, Hadoop) will achieve
prominence in enterprise data centers
 Shared storage, while controversial within some circles, can
be applied
 Communications bandwidth is as important a resource as
compute and storage
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© 2012 Evaluator Group, Inc.

21. © 2011 Emulex Corporation 21