Big data workloads are growing rapidly and require unprecedented scale in data storage, processing power and bandwidth. Cloud infrastructure provides the flexibility and economics needed to support big data's mixed workloads through software-defined compute, storage, networking and security. This allows organizations to gain real-time insights from massive data sets and harness big data's potential for industries like retail, manufacturing and more.

1. © 2009 VMware Inc. All rights reserved
Big Data: Now and in the Future
Is Your Cloud Ready for Big Data?
Richard McDougall
CTO, Application and Storage Services

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Not Just for the Web Giants – The Intelligent Enterprise

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Real-time analysis allows
instant understanding of
market dynamics.
Retailers can have intimate
understanding of their
customers needs.
Market Segment Analysis  Personalized Customer Targeting`

4. 4
The Emerging Pattern of Big Data Systems: Retail Example
Real-Time
Streams
Exa-scale Data Store
Parallel Data
Processing
Real-Time
Processing
Machine
Learning
Data Science
Cloud Infrastructure

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Storage: Plan for Peta-scale Data Storage and Processing
0.01
0.1
1
10
100
2000 2003 2006 2009 2012 2015
Online Apps
Analytics
PB of
Data
Analytics Rapidly Outgrows Traditional Data Size
by 100x

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Unprecedented Scale
“Data transparency,
amplified by Social Networks
generates data at a
scale never seen before”
- The Human Face of Big Data
We are creating an Exabyte
of data every minute in 2013
Yottabyte by 2030

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A single GE Jet Engine produces
10 Terabytes of data in one hour
– 90 Petabytes per year.
Enabling early detection of
faults, common mode
failures, product engineering
feedback.
Post Mortem  Proactively Maintained Connected Product

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The Emerging Pattern of Big Data Systems: Manufacturing
Exa-scale Data Store
Parallel Data
Processing
Real-Time
Processing Machine
Learning
Data Science
Cloud Infrastructure
Real-Time
Sensor
Analytics Support
Product
Engineering

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Cloud Infrastructure Supports Mixed Big Data Workloads
Machine
Learning HadoopReal-Time
Analytics
Cloud Infrastructure
Machine
Learning
Hadoop
Real-Time
Analytics
Management
Network/Security
Storage/Availability
Compute

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Software-defined Datacenter: Compute
Agility / Rapid deployment
Lower Capex
Isolation for resource control
and security
1
2
3
Operational efficiency4
Management
The Core Values of Virtualization Apply to Big Data
Network/Security
Storage/Availability
Compute

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Strong Isolation between Workloads is Key
Hungry
Workload 1
Reckless
Workload 2
Nosy
Workload 3
Cloud Infrastructure

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Management
Software-defined Datacenter: Storage
Requirements of Next Generation Storage
Network/Security
Storage/Availability
Compute
10x lower cost of storage
Handle explosive data growth
Support a variety of
application types
1
2
3
Solve the privacy and
security issues
4

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Software-defined Storage Enables Fundamental Economics
$-
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50
$4.00
$4.50
$5.00
$5.50
0.5 1 2 4 8 16 32 64 128
Cost per GB
Petabytes Deployed
Traditional
SAN/NAS
Distributed
Object
Storage
HDFS
MAPR
CEPH
Scale-out NAS
Isilon, NTAP

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Management
Software-defined Datacenter: Network and Security
Automate secure
network provisioning
Network & Security Requirements for Big Data
Network/Security
Storage/Availability
Compute
3
New high bandwidth
network designs
1
Leverage Software-defined
network security
2

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Big Data Requires Extreme Bandwidth

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Summary

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Customers Winning from Consolidated Big Data Platforms
“Dedicated hardware makes no
sense”
“Software-defined Datacenter
enables rapid deployment
multiple tenants and labs”
“Our mixed workloads include
Hadoop, Database, ETL and
App-servers”
“Any performance penalties are
minor”Management
Network/Security
Storage/Availability
Compute

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Cloud Infrastructure is Ready for Big Data – Are you?
Cloud Infrastructure

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Q&A