Overview of what is big data, the problem it creates for next-gen network, possible solution, the value prop and resources to learn more

1. Big Data Visibility with NPBs
Leveraging a big data model in the IT domain

2. What is big data?
Big data is high-volume, high-velocity and high-variety information
assets that demand cost-effective, innovative forms of information
processing for enhanced insight and decision making.
– Gartner
2© 2003-2014 VSS Monitoring. All rights reserved.

3. Network Data is a Big Data Problem
3.6Exabytes
Per Month by 2014
300x
Data Growth by 2020
70%
Server Workloads
Virtualized by 2014
Performance
Management
Security &
Forensics
Business
Intelligence
Network data & packet metadata are required for:
Only 0.5%
Network Packets Get Analyzed Outside of Operations
3© 2003-2014 VSS Monitoring. All rights reserved.

4. Today’s Network Analysis Architecture
Storage is tightly coupled with analytics
application
Data is stored in proprietary silos
Data is replicated across different toolsets
High CAPEX and OPEX
Problems
4© 2003-2014 VSS Monitoring. All rights reserved.
Expanded analysis capabilities
Storage scaling due to forensics
and compliance
Greater efficiency and agility
Business Needs

5. Analytics
Processing
Storage
Solution: Decouple Storage from Processing & Analytics
Analytics
Processing
Storage
5© 2003-2014 VSS Monitoring. All rights reserved.

6. Early Analytics Model
Collection, Storage and Presentation
functions are collapsed
Probes are highly distributed
6© 2003-2014 VSS Monitoring. All rights reserved.
File creation
Storage
Processing
and Retrieval
Framework
Querying and
Info Generation
Indexing
Visualization and Reporting

7. Analytics software tightly coupled
with hardware.
Storage and Presentation layers are
collapsed.
Probes are still distributed from
storage perspective.
Current Analytics Model
Collection has been decoupled from
Storage and Presentation layers
Capture is distributed, as proxy for
Analytics probes.
Capture and Grooming
File creation
Storage
Processing
and Retrieval
Framework
Querying and
Info Generation
Indexing
Visualization and Reporting
7© 2003-2014 VSS Monitoring. All rights reserved.

8. Visualization and Reporting
Availability of cheap, accelerated HW has
created a wedge between storage and
presentation layer.
Big data technologies using distributed
computing models (e.g. Hadoop) are
scaling analytics to massive quantities of
disparate data – beyond the capacities of
a distributed storage model (probe-based)
and are decoupling the Presentation /
Analytics layer from the underlying data
handling framework.
Purpose built HW and file creation
functions can be offloaded to hardware
based technologies, such as Network
Packet Brokers.
Capture and Grooming
File creation
Storage
Processing
and Retrieval
Framework
Querying and
Info Generation
Indexing
Next-Gen (big data) Analytics Model
8© 2003-2014 VSS Monitoring. All rights reserved.

9. Decoupling Software from Hardware
9© 2003-2013 VSS Monitoring. All rights reserved.
This is not a new story
“There’s no army that can hold
back an economic principle
whose time has come.”
– John Donovan, AT&T SEVP of
Technology and Network Operations

10. Big Data Visibility Architecture
Capture & forward directly to network tools
and Big Data platforms
Network tools analyze network packets (real
time and historical). Data is typically stored
in small increments in a proprietary format.
The Big Data Architecture allows tools to be
virtualized and run on alternative computing
environments such as Hadoop.

11. Value Proposition
Capture & Groom Analyze & PresentIngest & StoreGenerate
Larger Datasets Optimized Data Lower Cost Faster Retrieval Total Visibility
Addresses
storage as
neglected third
pillar of visibility
Scales Network
Analytics and
Forensics
Provides Network
Visibility to Big
Data Systems
11© 2003-2014 VSS Monitoring. All rights reserved.
Decoupling storage from analytics, allowing network data to be stored and
analyzed by tools of choice—including big data technologies and virtualized
probes—significantly lowers the TCO of packet capture for forensics, compliance
and data mining.

12. Learn more
“There are prodigious volumes of operational and business data available within network packet
streams, waiting to be fully leveraged by the fast growing ranks of Big Data analytics solutions.
By offering new methods for directly forwarding packet captures into Big Data architectures,
VSS Monitoring is opening a new door for operational-intelligence solutions.”
Jim Frey, Vice President of Research at Enterprise Management Associates
“By bringing together the Riverbed® Cascade® Shark continuous packet capture, analysis and
storage appliance with VSS Monitoring’s vBrokers powered by vSpool™, customers are able
to gain a new level of visibility, flexibility and control over their Big Data, resulting in significant
ROI and enhanced intelligence about their IT and network infrastructure.”
Dimitri Vlachos, Vice President, Marketing and Products at Riverbed
Register for 2-Part Webinars (Live & Recorded):
April 30, 2014 Leveraging a Big Data Model in the IT Domain,
Part 1 | Register: http://goo.gl/fCxUNK
May 7, 2014 Considerations for Ramping to a Big Data
Network Monitoring Architecture, Part 2
Register: http://goo.gl/CTTv2S
Read VSS Monitoring Press Release: http://goo.gl/OuZ1Q7
Visit Big Data Visibility solution page (QR Code)
12© 2003-2014 VSS Monitoring. All rights reserved.