In this webinar Storage Switzerland, Hitachi Data Systems and Brocade discuss why enterprises need to invest in big data analytics, how they can make that investment and what are some of the key requirements in designing a system.

1. Webinar
Improving Time to Value for
Enterprise Big Data Analytics
Join us as we explain:
● Why Enterprises Should Invest in Big
Data Analytics
● The Challenges Enterprises Face
When Standing Up a Big Data
Initiative
● Hitachi's HSP Hyper-Converged
Appliance
● Why The Hyper-Converged Network
Matters
For audio playback and Q&A go to:
http://bit.ly/EnterBigData

2. Welcome!
● All audio is through your speakers,
there is no dial-in
● A recording of this presentation and
others can be viewed at any time
without re-registering at:
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You can also access our
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3. Our Speakers
George Crump is the founder of Storage
Switzerland, the leading storage analyst focused on
the subjects of big data, solid state storage,
virtualization, cloud computing and data protection.
He is widely recognized for his articles, white papers,
and videos on such current approaches as all-flash
arrays, deduplication, SSDs, software-defined
storage, backup appliances, and storage networking.
He has 25 years of experience designing storage
solutions for data centers across the US.
Speaker bio

4. ● Analyst firm focused on storage, cloud
and virtualization
● Knowledge of these markets is gained
through product testing and interaction
with end users and suppliers
● The results of this research can be
found in the articles, videos, webinars,
product analysis and case studies on
our web site:
http://storageswiss.com
Who Is Storage Switzerland?

5. Our Speakers
Fred Oh is a Sr. Product Marketing Manager at Hitachi
Data Systems responsible for Big Data & Analytics
Infrastructure. His primary role is to develop and
execute go-to-market strategies for enterprise-class
hyper-converged scale-out systems. His secondary
function is to analyze industry trends and key
competitors’ product strategies, anticipating their
direction. Fred has a wealth of experience from working
on servers, operating systems and storage products.
Prior to joining Hitachi Data Systems, Fred held both
product management and software engineering roles at
NetApp, Acer, Unisys and HP. Fred majored in
Computer Science and Math at UC Davis. He is also an
avid Star Wars and Star Trek fan!

6. About Hitachi Data Systems
Hitachi Data Systems, a wholly owned subsidiary of Hitachi, Ltd.,
builds information management and Social Innovation solutions that
help businesses succeed and societies be safer, healthier and
smarter. We focus on big data that offers real value – what we call the
Internet of Things that matter. Our IT infrastructure, analytics, content
and cloud solutions and services drive strategic management and
analysis of the world’s data. Only Hitachi Data Systems integrates the
best information technology and operational technology from across
the Hitachi family of companies to deliver the exceptional insight that
business and society need to transform and thrive. Visit us at
www.HDS.com.

7. Our Speakers
Marcus Thordal leads a team of Technical Solution
Directors and Architects at Brocade, leaders in the field of
IT infrastructure solutions. He is known for understanding
customers’ business needs and industry trends to apply
these in combination when defining next generation
architectures and network solutions designs. He designs
IT infrastructure solutions based on 18 years of
experience which also includes presenting at industry
events, developing white papers and authorship of
Redbooks in the domain of Storage Networking, Storage
Virtualization & Disaster Recovery.

8. Brocade Key Facts
© 2016 BROCADE COMMUNICATIONS SYSTEMS, INC. 7
• Brocade is positioned as a
“Visionary” in the Gartner Magic
Quadrant for Data Center Network
Infrastructure
• R&D Centers in California,
Colorado, Minnesota, India,
Germany, and
the UK
• 17% of revenue re-invested in R&D
• 614 networking patents issued and
359 patent applications pending
worldwide
• Approximately 70% SAN switching
market share (Source: Dell’Oro)
• 40+ integrated UCP Solutions with
HDS, over a 15 year partnership
Technology Leadership
• 28,000+ networking
professionals trained annually,
with 17,500+ credentialed
Brocade Professionals
worldwide
• 450+ software engineers
focused on SDN and NFV
• Brocade is recognized for
providing “An Outstanding
Customer Service Experience”
(Source: JD Power)
• The #2 vendor in data center
networking market share
(Source: IDC)
• 10,000+ Ethernet Switches
sold with HDS
NASDAQ: BRCD

9. What Is Big Data Analytics?
• Where does Big Data
come from?
• How can Organizations
use Big Data?
• What do Big Data
Architectures look like?

10. CONFIDENTIAL – For use by Hitachi Data Systems employees and other audiences under NDA only.
Big Data
Volume / Variety / Velocity / Veracity
RAPIDLY CHANGING MARKET: DATA, TECHNOLOGY, COMPETITORS
HUMAN
DATA
EMAIL
WEB LOGS
DOCUMENTS
SOCIAL
10X
MACHINE
DATA
SATELLITE
IMAGES
BIO-
INFORMATICS
M2M LOG FILES
SENSORS
VIDEO
AUDIO
100X
STRUCTURED DATA
DECISION
SUPPORT
1X
Volume
Volume
Variety
Volume
Variety
Velocity
Data Analytics Drives Hitachi’s
Many Business
ANALYTICS
REAL TIME
HISTORICAL
METADATA
COMMAND &
CONTROL
PREDICTIVE
Volume
Variety
Velocity
Veracity

11. Why Are Enterprises Interested?
• Better Decisions
• Higher Efficiency
• Better Products

12. The Enterprise Hurdles To Big Data
Prosperity
• Lots of Assembly Required
• Open / Source Confusion
• Commodity Hardware
Confusion

13. What The Enterprise Needs
• Turnkey Solution that
Embraces Open
Concepts

14. Intro to HDS HSP
Scale-Out

15. © Hitachi Data Systems Corporation 2016. All rights reserved.14
Two Modern Data Architectures = Data Silos
1. Standard Hyper-Converged Infrastructure
(HCIS) is able to ingest and manage data
through Virtual Machines (VM).
• Unfortunately, data in each VM becomes
containerized and is not sharable.
• VMs limit the size of the data, and scalability.
• All data requests have to go through that
particular VM container, which limits parallel data
processing.
2. Big data analytics and Hadoop can
manage and process data in parallel.
• However, data is usually ingested from an
outside source, then copied into the Hadoop
Distributed File System (HDFS) for analytics
processing.
• Hadoop can only see and use the data that’s on
HDFS; nothing outside of it.
• Hadoop can only run Java-based applications
and scripts.

16. © Hitachi Data Systems Corporation 2016. All rights reserved.15
HSP Combines Both Architectures To Avoid Data Silos
▪ The Hitachi Hyper Scale-Out Platform (HSP) offers a unique
architecture that enables customers to do both.
▪ A hyper-converged appliance that can ingest data at speed, spin
up VMs on demand to execute applications that share data
between all VMs, and execute Hadoop jobs directly at the data
source along with other VMs.
▪ This makes the Hitachi Hyper Scale-Out Platform different from
anything else out there!

17. © Hitachi Data Systems Corporation 2016. All rights reserved.16
▪ Avoid Data Silos and Enable Sharing
Why Our Architecture is Ideal
NFS
Ingest
NFS
Ingest
Data Lake
NFS/
POSIX
AnalyticsHadoop
VM VMVM VM
NFS
Ingest
“Active and Elastic” “Blended”
PDI
HDP 2
mnt/nfs

18. © Hitachi Data Systems Corporation 2016. All rights reserved.17
Elastic Scaling of Big Data Applications
KVMKVMKVMKVMKVMKVMKVM
Elastic VM Management – Persistent Data
HSP
“Active” Data Lake – Global Namespace
Data-in-place
processing for
fast results
Efficiently run
analytics apps
at the data
source
Scale-out pay-
as-you-grow
“pop-up” VMs
and storage
Web Apps
Analytics
Applications
Hyper-converged, Scale-Out Appliance
Manage in
open cloud
environments
Scalable Enterprise File System

19. © Hitachi Data Systems Corporation 2016. All rights reserved.18 © Hitachi Data Systems Corporation 2016. All rights reserved.18
Evolving Enterprise Data Architectures
EDW Data
Marts
Analytics
Customer
Provisioning
Billing
BI Tools
Location
Web
Social
Media
Network
Hadoop
Cluster
NoSQL
Analytic
DB
On-Demand Integration & Blending
PDI
PDI
PDI
PDI
Pentaho Helps Bring Data Together

20. © Hitachi Data Systems Corporation 2016. All rights reserved.19
Automated and
Self-managed
Run Virtualized Apps
at the Data Source
Rapid
Data
Ingest
Data-in-Place
Analysis
Dramatically
Reduce Setup
Time
Accelerate Time to Insight
Value Proposition – Focus on Business Insight

21. © Hitachi Data Systems Corporation 2016. All rights reserved.20
Big Picture: HDS Big Data Infrastructure Portfolio
Data Lake
PDI
PDI
PDI
PDI
Enterprise Hyper-Converged Scale-out
PDI in KVMs
▪ Archive
▪ Regulatory
Compliance
▪ Analytics
▪ Hadoop/Spark
▪ NoSQL
▪ Elastic Search
PBA

23. 22
▪ Hitachi Hyper Scale-Out Platform for Big Data Analytics uses Brocade ICX
▪ Hitachi’s HSP with ICX 7750 Top of Rack
▪ 10/40 GbE Distributed switch
▪ Industry leading port-density
▪ Leading price/performance
▪ Low latency
▪ Cut-through
▪ Reliable, scalable, HA, efficient
https://youtu.be/3ohBDkJBJGs
The Network Matters for Hyper Scale-out

24. 23
• 2 Brocade ToR per rack
‒ 40G QSFP
• 20 nodes per rack
‒ (cluster 5 -100 nodes)
• Up to 5 racks
Start small ..and scale as you
go
HSP: Network Layout with Brocade ToR
Standard PDUs

25. 24
20 ports for nodes
6 ports for
rack to rack LAG pairs
6 port expansion
for more than 4 racks
1
2
3
4
5
6
Node
1
Node
3
Node
5
Node
7
Node
9
Node
11
Node
13
Node
15
Node
17
Node
19
Node
2
Node
4
Node
6
Node
8
Node
10
Node
12
Node
14
Node
16
Node
18
Node
20
1
2
3
4
5
6
Brocade ICX 7750-26Q 40G QSFP switch

26. The ICX Family
25
• 48-Port SFP+ 10 GbE
• 6+6-Port QSFP+/40 GbE
ICX7750-48FKey Highlights & Differentiators
• Unprecedented port density: 96×10
GbE /32×40 GbE per unit or 48×10 GbE
with up to 12×40 GbE uplinks/stack
ports
• Market-leading stacking scalability up
to 12 units per stack
• Longer stacking distance:
40 GbE (QSFP-LR)
• Chassis performance (~ 5.7 Tbps
aggregate stacking bandwidth)
• Hitless Failover within the stack
• SDN- Openflow 1.0 and 1.3 with hybrid
mode
• Full IPv4 and IPv6 routing support,
includes BGP and VRF
• Extended operating temp
(up to 50°C)
• Common Criteria and FIPs certification
• 48-Port RJ-45 10 GbE
• 6+6-Port QSFP+ (six optional)
ICX7750-48C
• 26 to 40 GbE ports QSFP+
• 32 to 40 GbE with optional module
ICX7750-26Q
• Quad-core 1.5 GHz CPU 8 GB system
memory, 2 G Flash, USB storage
• Optional 6×40 GbE QSFP
• Redundant power supplies and fans with
front-to-back or back-to-front airflow
All Brocade ICX7750 Models

27. Scale-out with Network Fabrics
26
• Topology freedom, east-west optimized
• All links active, L1/2/3 multipathing
• Fabric managed as one logical switch
• VM-aware
• Native and overlay network virtualization
Leaf/SpineCore
Scale-out
Fabric Architecture
CoreAggregationAccess
• Rigid architecture, north-south optimized
• Inefficient link utilization
• Individually managed switches
• VM-ignorant
• No network virtualization
Classic Hierarchical Architecture

28. Hyper Scale-out
Customer Example
27
http://finance.yahoo.com/news/japans-
largest-internet-company-deploys-
120000895.html

29. Thank you!
Storage Switzerland
http://www.storageswiss.com
gcrump@storage-switzerland.com
StorageSwiss on Twitter:
http://twitter.com/storageswiss
StorageSwiss on YouTube:
http://www.youtube.com/user/storageswiss
Hitachi Data Systems
http://www.hds.com
Hitachi Data Systems on Twitter:
http://twitter.com/HDScorp
Hitachi Data Systems on Facebook:
http://www.facebook.com/HitachiDataSystems
Brocade
http://www.brocade.com
Brocade on Twitter:
http://twitter.com/Brocade
Brocade on Facebook:
http://www.facebook.com/brocade

30. Improving Time to Value for
Enterprise Big Data Analytics
For Complete Audio and Q&A please register
for the On-Demand Version at:
http://bit.ly/EnterBigData