Keith Prabhu - Big Data Cloud Computing
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Keith Prabhu - Big Data Cloud Computing

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Presentation made at the Byte into Big Data Summit on November 21, 2013. It deals with the convergence of Big Data and Cloud Computing.

Presentation made at the Byte into Big Data Summit on November 21, 2013. It deals with the convergence of Big Data and Cloud Computing.

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Keith Prabhu - Big Data Cloud Computing Keith Prabhu - Big Data Cloud Computing Presentation Transcript

  • Big Data Computing Business Value Keith Prabhu Master of Business (Australia), CCSK, MBCI, CISSP, CISA Executive Director, Confidis Advisory Services Founder & Director, Cloud Security Alliance, Mumbai Chapter November 21, 2013 Technology Consulting www.confidis.co
  • STORYLINE Benefits & Challenges Cloud & Big Data Big Data Cloud Technology Big Data Trends www.confidis.co Security & Privacy Challenges
  • Big Data Trends • • • • Data is becoming more valuable • No longer a question of “what to store?” but rather “what can we do with the data?” – Implication increased data volumes • As per Gartner: • Enterprise data will grow by 800% from 2011 – 2015 • 80% will be unstructured, 20% structured • Need for combining diverse data sets Data Analytics is moving from batch to real time • Real time analytics technology is part hype stage and is in early adoption Emergence of predictive analytics • Real time analytics provides organizations predictive capabilities Scope of Big Data continues to expand • Machine data, Sensors etc. • Ever increasing number of applications of Big Data for Enterprises e.g. Customer churn analysis, flexible supply chains, loyalty pricing, ad targeting, POS transaction analysis etc.. www.confidis.co BIG DATA BIG Problem or BIG Opportunity
  • Cloud & Big Data – Perfect Allies! Big Data Requirements Cloud Computing Capabilities Need for significant infrastructure investments Pay as you go Cloud model is ideal for enterprises “to think big but start small” Rapid storage scalability Easy storage scalability Rapid provisioning of processing power On the fly provisioning of additional processing power Interoperability with other data sources (internal & external) Provides a plethora of interfaces to connect to other platforms Quick deployment options to get quick insights Availability of Analytics-As-A-Service (AaaS) Big Data & Cloud Computing are complementary www.confidis.co
  • Big Data Cloud - Technology Service Models Deployment Models IaaS: You get barebones IT infrastructure, you need to install and manage the Big Data stack Private: Big Data stack is deployed on infrastructure that is dedicated solely for your use PaaS: You get the development platform which can be used to develop analytics applications Public: Big Data stack is deployed on a public cloud that uses shared resources SaaS (AaaS) You get ready to run analytics application. Plug in your data and switch on! Hybrid: Big Data stack is deployed across private and public clouds Big Data & Cloud Computing are complementary www.confidis.co
  • Big Data Cloud – Benefits & Challenges Options Public Cloud Leverage Public Data PaaS Frameworks like Amazon Elastic MapReduce Implications • • Pros & Cons Easy to get up and running e.g. Amazon map reduce jobs (EMR, EC2) + Easy and low cost External data upload requirement poses security concerns - + Can be used to study the map reducibility problem Limited Public Data PaaS options available - Vendor lock-in • Leverage Public IaaS and setup Big Data Solutions on them www.confidis.co Significant size and processing times impacts latency • Requires on-premise Map + No procurement lead times Reduce Cluster + No vendor lock-in Data Transfer + May be cloud-neutral requirement remains the - Perimeter security concerns, data same transfer speed (more time to run) and high latency limitations apply • - Perimeter security concerns, data transfer speed (more time to run) and high latency limitations apply
  • Big Data Cloud – Benefits & Challenges Options Private Cloud Build the Map Reduce clusters on a set of virtual machines Implications • • Provision VMs on shared infrastructure Setup Map Reduce clusters on shared storage infrastructure Pros & Cons + Easier provisioning + Dynamic scale out of infrastructure - Shared infrastructure and virtualization overheads may impact latency • Build out the Map Reduce clusters on its own dedicated hardware Overheads of virtualization and limitations of architecture • Provision dedicated hardware + High performance, low latency • Build out Map Reduce clusters on a share nothing setup - Increased cost with lower utilization of dedicated hardware - Procurement lead time Source: http://www.infosys.com/cloud-services/resources/pages/big-data-spectrum.aspx www.confidis.co
  • Security - Issues www.confidis.co Source: Top Ten Security and Privacy and Security Challenges for Big Data, Cloud Security Alliance
  • Cloud Security Alliance • • • • Global, not-for-profit organization Over 45,000+ individual members, 100+ corporate members Building best practices and a trusted cloud ecosystem Agile philosophy, rapid development of applied research − GRC: Balance compliance with risk management − Reference models: build using existing standards − Identity: a key foundation of a functioning cloud economy − Champion interoperability − Advocacy of prudent public policy “To promote the use of best practices for providing security assurance within Cloud Computing, and provide education on the uses of Cloud Computing to help secure all other forms of computing.” Join: Cloud Security Alliance, Mumbai Chapter on LinkedIn (http://www.linkedin.com/groups?gid=2963138) www.confidis.co
  • Thank You! www.confidis.co
  • Contact Us For any further information, please contact: www.confidis.co Keith Prabhu Executive Director Confidis Advisory Services Private Limited Email: info @ confidis DOT co