Big Data Visibility with NPBs
Leveraging a big data model in the IT domain
What is big data?
Big data is high-volume, high-velocity and high-variety information
assets that demand cost-effective, i...
Network Data is a Big Data Problem
3.6Exabytes
Per Month by 2014
300x
Data Growth by 2020
70%
Server Workloads
Virtualized...
Today’s Network Analysis Architecture
Storage is tightly coupled with analytics
application
Data is stored in proprietary ...
Analytics
Processing
Storage
Solution: Decouple Storage from Processing & Analytics
Analytics
Processing
Storage
5© 2003-2...
Early Analytics Model
Collection, Storage and Presentation
functions are collapsed
Probes are highly distributed
6© 2003-2...
Analytics software tightly coupled
with hardware.
Storage and Presentation layers are
collapsed.
Probes are still distribu...
Visualization and Reporting
Availability of cheap, accelerated HW has
created a wedge between storage and
presentation lay...
Decoupling Software from Hardware
9© 2003-2013 VSS Monitoring. All rights reserved.
This is not a new story
“There’s no ar...
Big Data Visibility Architecture
Capture & forward directly to network tools
and Big Data platforms
Network tools analyze ...
Value Proposition
Capture & Groom Analyze & PresentIngest & StoreGenerate
Larger Datasets Optimized Data Lower Cost Faster...
Learn more
“There are prodigious volumes of operational and business data available within network packet
streams, waiting...
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Leveraging a big data model in the IT domain

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

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Leveraging a big data model in the IT domain

  1. 1. Big Data Visibility with NPBs Leveraging a big data model in the IT domain
  2. 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. 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. 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. 5. Analytics Processing Storage Solution: Decouple Storage from Processing & Analytics Analytics Processing Storage 5© 2003-2014 VSS Monitoring. All rights reserved.
  6. 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. 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. 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. 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. 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. 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. 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.

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