Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.

1. The Big Data
Landscape

2. Entering a New Era of Scale
2

3. Convergence of Technology Disrupters
Create Opportunity
NetApp Confidential - Internal Use Only
Cloud
SocialMobile
Internet of
Things
Big Data

4.  Traditional Structured and
Replicated Data mix shift is
driven by:
− Efficiency (Dedup,
Compr, Thin Prov, SATA)
− Growth in new category
of storage consumers
using cloud / content
depots
 Unstructured Data (files
and objects) in traditional
storage + Content depots /
Cloud) will be the largest
storage category by 2014
− Content depots / Cloud
expected to be 95%
unstructured data
Revenue Share by Segment
Traditional structured
Traditional replicated
Traditional unstructured
Content depots / public cloud
Unstructured Data Growth Dominates

5. Not Even to The “Peak”
Estimated size of the
digital universe in 2020
40 Zettabytes 5 Billion
Smart phones
30 Billion
Pieces of new content to
Facebook per month
5
Technology Trigger
Peak of Inflated Expectations
Trough of Disillusionment
Slope of Enlightenment
Plateau of Productivity
VISIBILITY
TIME
80%
Unstructured
data

6. Big Data Is All Data From Everywhere
 Transactional Data
 Machine Data
 Social Data
 Enterprise Content
Fundamentally changes your business
The Jet way
The Call Center

7. Big Data Vendor Landscape
A Lot of Hype and Buzz – Everyone is Jumping In
 Market is expected to grow from $3.2 billion
in 2010 to $16.9 billion in 2015
 NoSQL $2Bn PA by 2015
 Most firms are taking a pragmatic approach
 Big data is in the very early stages of maturity
 Best practices are not mature
IDC Big Data Survey
7
Nov-11
400
350
300
250
200
150
100
50
0
Jan-08
Cloudera series B
MapR series A
Cloudera series C
10gen series D
MapR series B
DataStax series B
Neo Technology series A
Opera Solutions series A
Platfora series A
Couchbase series C
Cloudera series D
Funding for Hadoop and NoSQL
"The Big Data market is expanding rapidly …
For technology buyers, opportunities exist to
use Big Data technology to improve
operational efficiency and to drive innovation.
Use cases are already present across
industries and geographic regions."
Dan Vesset, Vice President, IDC
451 Research

8. Data Growth Impact on Business
8
Complexity
VolumeSpeed
BusinessVelocity
Inflection
Point
Information Becomes
a Propellant to Business
Data Becomes a
Burden to IT Infrastructure
2010 2020
“Big Data” refers to datasets whose size is
beyond the ability of typical tools to capture,
store, manage and analyze

9. Why Should You Care?
It’s the Value of Your Data
 Top line revenue
– Leverage their data
assets into business
advantage
 Bottom Line savings
– Lower the cost of
compliance
– Manage ever growing
data efficiently
 Over 1PB of data
 Growth of 175% YOY
 90 days of data within
 24 hours of a failure
 5 Billion Records
 Anywhere, Anytime
 Faster time to market
 50% Increase in Revenue
9

10. NetApp Big Data

11. Why NetApp?
Practical solutions that solve today’s problems
Get
Control
NetApp helps you turn your
exploding data from threat to
opportunity. Manage your data
effectively and affordably.
Break
Through
Break through the limits. With
NetApp, you can take on even the
most massive and complex data
projects.
Gain
Insight
Turn insight to action. NetApp helps
you get to clarity and insight faster
and more reliably.
11

12. Experience Managing Data at Scale
12
100 Customers
50 Customers
10 Customers
4 Customers
100 PB
50 PB
20 PB
10 PB
NetApp’s Largest Customer

13. NetApp Big Data Strategy
 Best of breed storage for Big
Data Applications
 Create deep integration and
value add
 Build on open standards with
best-in-class partnerships
 Validate with Ecosystem
Leaders
– Complete server, network and
storage “Racks”
– Delivered via trusted high-value
partners
13
Open
Best-of-Breed
Choice

14. Industry-Leading Storage Innovation
14
Flash Arrays
for ultra-high performance
E-Series
for price-performance at scale
StorageGRID
for web scale object storage
Clustered Data ONTAP
for Shared Infrastructure
Corporate
Data Centers
Cloud
Data Centers

15. Big Content
Retain forever, multi-site distribution
Big Bandwidth
Ingest, Process, Stream
Big Analytics
Reduce, Analyze, Report
Cloud
Private/Public
Retain, Distribute
Big Data Building Blocks
Applications
Extract
Retain, Distribute
Store
Retrieve
15

16. 16

17. Analytics Oriented Business Processing
RDBMS
General Purpose DB
 Data organized to
align with schemas
 Fixed consistency
model
 Complex queries
supported
 Volume based data
management
Columnar DB
Analytics Oriented
 Data organized in
column files
 Tabular interface
without rigid schemas
 Fast column scans
 Multiple consistency
models
 Transaction granular
data management
Document Store
Transaction Oriented
 Data organized in
data structures in
memory
 Schemaless
transaction store for
structured data
 High transactional
performance
K-V Store
Metadata Service
Oriented
 Data organized in key
value pairs
 Suitable for metadata
services with CMS’
 Associated with
object services
Transaction Processing
Realtime Analytics
Business Applications
Memory Ingest
Disk/Flash Tier
Query-based
Retrieval
Commit
Federated Database Store
(Build/Buy/Partner)
Persisted
Commit
Transaction granular data
resilience, recoverability &
protection at line speeds
Data organization
optimized by query
interface
Performance
optimized query
service

18. Analytics Technologies to look out for!
Columnar
DBs
(Analytics
Oriented)
Document
Stores
(Transaction
Oriented)
Key-Value
Stores
(Content/Object
Service)
Graph
DBs
(Niche)
Relational DBs
Row-oriented
RDBMS’
Datacenter Multi - Datacenter
• ACID constrained
• Complete query set
• Limited availability
• High consistency
• Rich query set
• Good availability
• Tuneable consistency
• Limited query set
• Highest/WAN availability
Old World New World

19. Analytics & Enterprise Apps Environment
19
Sensors
Applications
Logs
Location/GPS
Mobile Devices
Storage
(All other storage, i.e. internal DAS)
Content
Repositories
Shared Storage
Infrastructure
Storage File Systems
Data Management
Analytics
Applications
Reporting/Dashboard/Visualization
ETL
OLAP
OLTP
Other
Data
Sources
OLAPETL
Storage
Data
Management
NFS/sNFS/pNFS
NetApp Confidential – Limited Use

20. Some problems require an Enterprise Class
Hadoop solution
20
Enterprise Class Hadoop
Packaged ready-to-deploy modular Hadoop
cluster
 The data has intrinsic value $$$
 Capacity and compute requirements
expanding very fast
 Higher storage performance
 Real human consequences if the system
fails (Threats, treatments, financial losses)
 System has to allow for asymmetric growth
Commodity, Off the Shelf Hadoop
Values associated with early adopters of
Hadoop
 Social Media Space
 Contributors to Apache
 Strong bias to JBOD
 Skeptical of ALL vendors
Enterprise Class Hadoop
Packaged ready-to-deploy modular compute
intensive Hadoop cluster
 Compute intensive applications
 Video, imaging analysis
 Extremely tight Service Level expectations
 Severe financial consequences if the
data analytic application or service is
run late
Enterprise Class Hadoop
Packaged ready-to-deploy modular storage
intensive Hadoop cluster
 Storage intensive applications
 Additional CPUs does not help run time
 Financial ticker data analysis
 Extremely tight Service Level expectations
 Need deeper storage per datanode
ComputePower
Storage Capacity
NetApp Confidential – Limited Use

21. 21
NetApp Open Solution for Hadoop
 Easy to Deploy, Manage and Scale
 Uses High Performance storage
– Resilient and Compact
– RAID Protection of Data
– Less Network Congestion
 Raw Capacity and density
– 120TB or 180TB in 4U
– Fully serviceable storage system
 Reliability
– Hardware RAID & hot swap prevent
job restart due to node go off-line in
case of media failure
– Reliable metadata (Name Node)
Enterprise Class Hadoop
Map
Reduce
NameNode
DataNodes /
TaskTracker
DataNodes /
TaskTracker
:
HDFS
Secondary
NameNode
4 separate shared
nothing partitions
NetApp Confidential – Limited Use
JobTracker
FAS2040
E2660

22. NetApp Open Solution for Hadoop
Validated Benefits for the Enterprise
 Improved cluster performance by 62%
 Completed jobs 200% faster under
drive failure
 Delivered linear performance scalability
as nodes, data grew
 Per-server capacity increase of 1.5x
The NetApp Open Solution for Hadoop improves capacity
and performance efficiency and recoverability compared to
a server-based DAS deployment.
- ESG, 2012

23. Optimizing Performance and Stay Healthy
23
Source: Garrett, Brian and Lockner, Julie, “NetApp Open Solution for Hadoop”, ESG Report,
May 2012, http://bit.ly/LyYG0t
Network Overhead Useful Work
Availability and
Resiliency
Burst Handling and
Queuing
Oversubscription
Ratio
Data Node Network
Speed
Network
Latency
Source: Cisco: http://bit.ly/yL54Ts

24. DAS vs. NetApp footprint
DAS Option
 2RU, CPU: 2x8 cores, RAM: 48GB, Disk:
24 TB
 1 Rack(42RU): 20 servers (320 cores,
960GB, 480TB)
 6 Racks: 1920 cores, 5.7TB RAM, 2.8 PB
Storage (120 servers)
NetApp Option
 1RU, CPU: 2x8 cores, RAM: 48GB, Disk: 2
TB (8TB Max(Optional PIXI Boot Diskless)
 1 Rack (42RU)
 CPU and Memory: 24 servers(6:1),
384 cores, 1.152TB
 Storage: 4 E2660 720TB
 4 Racks: 1536 cores, 4.6TB, 2.8 PB (96
servers)

25. Case Study: ASUP NetApp Analytics
25
Gateways
• 800K ASUPs
every week
• 40% coming
over the
weekend
Extract
Transform
Load
Data
Warehouse Data Mart
Data Mart
ETL
• Data needs
to be
parsed
and loaded
in 15
minutes
Data Warehouse
• Only 5% of data goes into
the data warehouse, rest
unstructured, yet it’s growing
7-10 TB per month
• No easy way to access this
unstructured content
Reporting
• Numerous mining
requests are not
satisfied currently
• Huge untapped
potential of
valuable insight
Finally, the incoming load doubles every 16 months!
NetApp Proprietary - Limited Use Only

26. Case Study: NetApp Large-Scale Analytics
CHALLENGE
NETAPP
SOLUTION
BENEFITS
4 weeks to run a query
on
24 billion unstructured
records
10-node
Hadoop
Cluster
Time reduced from
4 weeks to 10.5
hours
Impossible to run a
query:
240 billion unstructured
records
Previously
impossible, now
achievable in just 18
hours
26NetApp Proprietary - Limited Use Only

27. Big Data System Integrators Solutions Built on NetApp®
Integrated Big Data Solutions and Expertise
 Planning and implementation expertise for Big Data
 Turn-key solution stacks and Big Data services
27

28. Next Steps - Team with the Experts
 Strategic Assessment
– Business goals
– Data growth needs
– Use case discovery (partner
delivery)
 Consult
– Solution architecture and design
(NetApp delivery)
 Deploy
– Installation and implementation
(NetApp delivery)
– Solution implementation (partner
delivery)
28
Support options:
Global support available
from NetApp and partners

29. NetApp Confidential - Internal Use Only