Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (Infrastructure CTO, VMware)


Published on

Over the last few years we’ve seen a frenzy of interest and buzz around the area of Big Data. Beyond the hype, there is a solid base of growing use cases, which are becoming center stage to most businesses. 2012 was the year of awareness. There was a great amount of sharing from the early core developers of the analytic platforms – showing the rest of the world the capabilities of the tools and platforms that had been developed for special purpose high scale analytics. The big names at the core of open source analytics development include Facebook, eBay, Linkedin, Twitter – all blazing the trail with new approaches. These companies brought along with them a new and expanding interest in leveraging the same technologies for commercial interest.

This talk is focused at how a growing number of enterprises that are already heavily invested in the use cases – but by volume, most customers now have some form of big data proof-of-concept underway. These proof of concepts typically start with a thesis of how competitive advantage can be gained through insight from the data. A proof of concept can quickly validate the theory, and helps sell further investment in the analytics platform, and it snowballs from there.

Published in: Technology
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • There are a number of characteristics that define true network and security virtualization, with the four key points being:Completely decouples virtual networks from physical network hardwareFaithfully reproduces the physical network and security  model in the virtual network space, workloads see no differenceAutomation, from both a cloud operations and network operations perspective.And, a platform that preserves choice
  • Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (Infrastructure CTO, VMware)

    1. 1. © 2009 VMware Inc. All rights reservedBig Data: Now and in the FutureIs Your Cloud Ready for Big Data?Richard McDougallCTO, Application and Storage Services
    2. 2. 2Not Just for the Web Giants – The Intelligent Enterprise
    3. 3. 3Real-time analysis allowsinstant understanding ofmarket dynamics.Retailers can have intimateunderstanding of theircustomers needs.Market Segment Analysis  Personalized Customer Targeting`
    4. 4. 4The Emerging Pattern of Big Data Systems: Retail ExampleReal-TimeStreamsExa-scale Data StoreParallel DataProcessingReal-TimeProcessingMachineLearningData ScienceCloud Infrastructure
    5. 5. 5Storage: Plan for Peta-scale Data Storage and Processing0.010.11101002000 2003 2006 2009 2012 2015Online AppsAnalyticsPB ofDataAnalytics Rapidly Outgrows Traditional Data Sizeby 100x
    6. 6. 6Unprecedented Scale“Data transparency,amplified by Social Networksgenerates data at ascale never seen before”- The Human Face of Big DataWe are creating an Exabyteof data every minute in 2013Yottabyte by 2030
    7. 7. 7A single GE Jet Engine produces10 Terabytes of data in one hour– 90 Petabytes per year.Enabling early detection offaults, common modefailures, product engineeringfeedback.Post Mortem  Proactively Maintained Connected Product
    8. 8. 8The Emerging Pattern of Big Data Systems: ManufacturingExa-scale Data StoreParallel DataProcessingReal-TimeProcessing MachineLearningData ScienceCloud InfrastructureReal-TimeSensorAnalytics SupportProductEngineering
    9. 9. 9Cloud Infrastructure Supports Mixed Big Data WorkloadsMachineLearning HadoopReal-TimeAnalyticsCloud InfrastructureMachineLearningHadoopReal-TimeAnalyticsManagementNetwork/SecurityStorage/AvailabilityCompute
    10. 10. 10Software-defined Datacenter: ComputeAgility / Rapid deploymentLower CapexIsolation for resource controland security123Operational efficiency4ManagementThe Core Values of Virtualization Apply to Big DataNetwork/SecurityStorage/AvailabilityCompute
    11. 11. 11Strong Isolation between Workloads is KeyHungryWorkload 1RecklessWorkload 2NosyWorkload 3Cloud Infrastructure
    12. 12. 12ManagementSoftware-defined Datacenter: StorageRequirements of Next Generation StorageNetwork/SecurityStorage/AvailabilityCompute10x lower cost of storageHandle explosive data growthSupport a variety ofapplication types123Solve the privacy andsecurity issues4
    13. 13. 13Software-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$5.500.5 1 2 4 8 16 32 64 128Cost per GBPetabytes DeployedTraditionalSAN/NASDistributedObjectStorageHDFSMAPRCEPHScale-out NASIsilon, NTAP
    14. 14. 14ManagementSoftware-defined Datacenter: Network and SecurityAutomate securenetwork provisioningNetwork & Security Requirements for Big DataNetwork/SecurityStorage/AvailabilityCompute3New high bandwidthnetwork designs1Leverage Software-definednetwork security2
    15. 15. 15Big Data Requires Extreme Bandwidth
    16. 16. 16Summary
    17. 17. 17Customers Winning from Consolidated Big Data Platforms“Dedicated hardware makes nosense”“Software-defined Datacenterenables rapid deploymentmultiple tenants and labs”“Our mixed workloads includeHadoop, Database, ETL andApp-servers”“Any performance penalties areminor”ManagementNetwork/SecurityStorage/AvailabilityCompute
    18. 18. 18Cloud Infrastructure is Ready for Big Data – Are you?Cloud Infrastructure
    19. 19. 19Q&A