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Hadoop: Making it work for the Business Unit


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Hadoop: Making it work for the Business Unit

  1. 1. The information provided in this document constitutes confidential and proprietary information of Zettaset, Inc. You may not disclose, use, reproduce or distribute this document (or any portion thereof) without Zettaset's prior written authorization. Further, as between you and Zettaset, Zettaset owns all right, title and interest in and to this document (together with any and all related intellectual property rights). Zettaset Hadoop: Making it Work in the Business Unit Jim Vogt, President & CEO, Zettaset Hadoop Summit - June 5, 2014
  2. 2. View from the Business Unit… • Customer focus is shifting to the top layers of the big data software stack, from information management to the “analytics & discovery” and “applications” layers
  3. 3. Hadoop in its Infancy • Early Hadoop adoption was driven by cost savings • Hadoop’s value proposition to enterprise customers has expanded to include flexibility, analytics, and discovery capabilities • As Hadoop continues to mature, the stack of applications and business processes that can work with data directly in Hadoop’s file system is growing, driving a virtuous cycle of adoption • Hadoop becoming increasingly strategic and mission critical to enterprise computing: Potential to become the primary data management technology 3
  4. 4. • As key enterprise issues with Hadoop are addressed through technology, Hadoop will emerge as the primary data store • Cost-effective, powerful, flexible and secure 4 Hadoop Emerging as Primary Data Store
  5. 5. Big Data Adoption Barriers 5 • Given its relative immaturity, customers face multiple issues with Hadoop deployments, including security, reliability, application integration, dependence on professional services • Lack of best practices for integrating Big Data analytics into existing business processes and workflows • Vendors racing to address customer challenges with new solution capabilities Security for big data will be a key issue in 2014 and beyond. Merv Adrian, Gartner blog - March 2014
  6. 6. 6 Security is #1 Technology Challenge Facing Organizations with Big Data Initiatives* * Source: IDG Enterprise Big Data Study, 2014 Sample: 751 companies
  7. 7. Data Security: Key to Accelerating Growth* 7 * Source: Ovum Security controls used to protect against insider attacks by number of respondents Number of respondents with concerns about big data issues 59% 57% 55% 0% 20% 40% 60% 80% Lack of visibility into the security measures used by the SaaS or Cloud Provider Potential for other users of the service to access my organization's data Lack of control over the location of data Percentage responses for the top three cloud and SaaS usage concerns • Security in particular is a key focus for enterprise customers considering Big Data solutions • Enterprises face severe commercial and reputational risk from data breaches • Enterprise customers will not deploy Hadoop to manage sensitive data until vendors secure such infrastructure
  8. 8. Big Data Highly Services Dependent* * Source: Wikibon, February 2014 * Big Data Revenue by Type, 2013 (in $US millions) (n=$18,814) • Challenging to scale services-based business models • Software projected to have the fastest growth rate out of the three segments • Market will shift to software because its value proposition is automated and replicable as the technology matures
  9. 9. Synergy Between Big Data and Cloud • Virtually unlimited data storage scale-out • Multiple applications and “as-a-service” offerings supportable • Point of integration with third-party data sources • Service and capacity on- demand, any time, anywhere 9 Source: CSC Data security, reliability, and performance remain key enterprise requirements, no matter where or how Big Data / Hadoop is deployed
  10. 10. • Each of these attributes represents a challenge for organizations driving Big Data initiatives Five Most Important Attributes of a BI / Analytics / Big Data Solution* * Source: Enterprise Strategy Group - April 2014
  11. 11. Analytics Pulling the Market “This is a time of accelerating change, where your current IT architecture will be rendered obsolete. Leading organizations of the future will be distinguished by the quality of their predictive algorithms.” - Peter Sondergaard, Gartner
  12. 12. • Comprehensive security, including access control and data encryption • Response time not affected by security controls, no impact on user experience • High availability ensure the reliability and stability of the database • Data access via easy-to-use graphical user interfaces, no need to write code • Advanced analytic capabilities to analyze multi-structured data • Sophisticated visualizations to understand and make sense of Big Data • “Speed-of-thought” performance, a cumulative measure of all components What Analytics Users Want in a Big Data Solution
  13. 13. Multi-National Financial Services Organization •Automate and simplify Hadoop installation and cluster expansion/scalability •Easy integration with Active Directory security policy, and data access control •Simplify and secure Hadoop connectivity to BI and analytics applications Major Healthcare Provider •Secure protected health information and patient records •Assist with HIPAA compliance, lock down sensitive data with encryption •Automate administration and security across multiple locations Leading Online Payments Company •Fine-grained, role-based access control and support for multi-tenancy •High availability and automated fail-over for on-demand service reliability •Activity monitoring and logging for SLA reporting 13 Enterprise Requirements Examples
  14. 14. Use Case – Financial Services • Banks and credit card companies want to be able to analyze years of transaction history to investigate and predict fraudulent transactions, detect purchase patterns of consumers and score individuals on credit worthiness • Depth of this transactional history ranges from hundreds of Terabytes to several Petabytes of data, making it cost prohibitive for traditional databases • Hadoop proving to be a more cost-effective and scalable storage and data access solution • However, securing consumer financial data in Hadoop is of paramount importance to financial institutions, who must comply with data protection and privacy mandates such as PCI/DSS and SOX
  15. 15. Use Case – Healthcare Records • Electronic healthcare records are vulnerable to both insider and outsider threats because of the value of information to criminals • Physicians notes are an example of unstructured data that is retained by healthcare organizations • When combined, this information represents highly sensitive 'regulated data,' which is tightly controlled by federal laws as well as numerous state breach notification laws • HIPAA - Health Insurance Portability and Accountability Act addresses the privacy and security of patient data 15
  16. 16. Use Case – Retail Payments • Online payments company combines the use of Hadoop databases with analytics for merchant reporting, along with dashboard applications that analyze merchant-specific payments • Data security as well as service reliability is of utmost importance in this environment • Transactions involve a database that includes personally-identifiable information for millions of users, and the system must be available on- demand, 24 x 7 • Requirement to secure one merchant’s data from the data of others, and that requires multi- tenancy, supported by sophisticated role-based access control
  17. 17. Hadoop: Meeting Business Unit Expectations Focus on business applications and processes vs. database mechanics Comprehensive security approach that simplifies integration with existing enterprise security frameworks Simplified application integration, including analytics High reliability across all critical Hadoop services More process automation, and fewer requirements for professional services Hadoop that’s enterprise-ready 17
  18. 18. Thank you!