Big security with Big Data: myths and truths behind the hype surrounding Big Data deployments for security
 

Big security with Big Data: myths and truths behind the hype surrounding Big Data deployments for security

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Big Data trends are visible within many large IT organizations. Companies are realizing that platforms such as Hadoop are well suited for large-scale analytics that require significant processing ...

Big Data trends are visible within many large IT organizations. Companies are realizing that platforms such as Hadoop are well suited for large-scale analytics that require significant processing power and run on petabytes of data. Learn how these practices can be applied in a security context--separating facts from fantasy. You'll see the most common use cases utilizing HP security products as the integration points for Big Data analytics, specifically the HP SIEM platform and related components.

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Big security with Big Data: myths and truths behind the hype surrounding Big Data deployments for security Big security with Big Data: myths and truths behind the hype surrounding Big Data deployments for security Presentation Transcript

  • Big data – myths and truths Roopak Patel, Product Management © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Agenda • • • • • 2 History of big data Big data opportunities Big data myths Big data truths What to do? © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • History of big data Where we’ve been… © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Accelerating innovation and time to value SolidFire kaggle Music iHandy SuperCam Pandora Xactly DCC Manufacturing Projects Product Configurator SAP CRM Bills of Material SCM Quality Control Burroughs IBM Hitachi Hyland Hosting.com Ariba Zoho LimeLight Unisys NEC Gigabytes Megabytes Cash Management ERP Bull Time and Expense Fijitsu Costing Payroll Microsoft HCM OpSource Accounts Receivable Sales tracking & Marketing Adobe Rostering PPM Service Claim Processing Database Data Warehousing Saba Intacct Kenexa Saba Softscape IntraLinks SCM PaperHost Renren FinancialForce.com Toggl Fring News Xing Cookie Doodle Rackspace Flickr dotCloud New Relic Mozy Utilities Zynga Associatedcontent Atlassian Qzone Tumblr. Ah! Fasion Girl MobilieIron PingMe BeyondCore Productivity Fed Ex Mobile Twitter TripIt Paint.NET 4 1,820TB of data created Jive Software Amazon MailChimp SmugMug CYworld Business myHomework NetSuite Exact Online Social Networking YouTube 168 million+ emails sent Viber Answers.com RightScale MobileFrame.com 698,445 Google searches Yammer Entertainment Atlassian BrainPOP Sonar6 Photo & Video Heroku Zillabyte SuccessFactors Education Sonar6 Mixi Yandex Navigation Khan Academy Kinaxis Softscape Volusion Workday Baidu iSchedule SugarCRM Quadrem Cornerstone onDemand Zynga SLI Systems Yahoo! Yahoo Microsoft 11million instant messages Twitter Zettabytes Elemica CyberShift Corel PLM Time & Attendance Commissions Avid ADP VirtualEdge Billing Activity Management Training Serif Xerox Fixed Assets Workbrain 695,000 status updates Pinterest Mobile, social, big data & the cloud The internet Client/server Mainframe Kilobytes ScaleXtreme Games CloudSigma HP ePrint Sport Yandex cloudability nebula CyberShift box.net Sage Workscape Cost Management Splunk Hootsuite Qvidian OpenText Lifestyle Atlassian Amazon Web Services Foursquare Datapipe Alterian 98,000+ tweets Taleo Reference PingMe Bromium Scanner Pro NetReach EMC Travel Parse LinkedIn buzzd NetDocuments Quickbooks Order Entry Inventory HCM GoGrid Tata Communications MRM Engineering eBay CCC HP AppFog UPS Mobile Facebook Google Finance Urban Dragon Diction Plex Systems Every 60 seconds SmugMug salesforce.com Snapfish NetSuite Joyent Scribd. Amazon DocuSign © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 217 new mobile web users Yottabytes
  • Innovative companies are changing rules of the game All driven by the power of big data • • • • Develop disruptive business models Create better products and services Enhance customer experience Drive sustained competitive advantage Leverage your data: make it matter 5 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 2012
  • Growth of data to accelerate Exponential rise Data is growing at a 40% compound annual rate, reaching nearly 45 ZB by 2020 50 45 40 35 30 25 20 15 10 5 0 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Source: Multiple 6 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Big data hype cycle We cover the spectrum of use-cases and growth paths for Security Visibilithy Peak of Inflated Expectations Slope of Enlightenment Plateau of Productivity Trough of Disillusionment Technology Trigger Maturity Source: Gartner 2013 7 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Big data opportunities across industries and use cases Innovative analytic use cases are cutting across structured, semi-structured, and unstructured data Finance Government Telecom • Fraud detection • Law enforcement • Broadcast monitoring • Anti-money laundering • Counter terrorism • Churn prevention • Risk management • Traffic flow optimization • Advertising optimization Manufacturing • Supply chain optimization • Defect tracking Energy Healthcare • Weather forecasting • Drug development • Natural resource exploration • Scientific research • RFID Correlation • Evidence based medicine • Warranty management • Healthcare outcomes analysis Horizontal use cases • Sentiment analysis • Logistics optimization • Social CRM/network analysis • Brand management • Clickstream analysis • Churn mitigation • Social media analytics • Influencer analysis • Brand monitoring • Pricing optimization • IT infrastructure analysis • Cross and up sell • Internal risk assessment • Legal discovery • Loyalty and promotion analysis • Customer behavior analysis • Equipment monitoring • Web application optimization 8 • Marketing campaign optimization • Revenue assurance • Enterprise search © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Sources: IDC: 2012 “Worldwide Big Data Technology and Services Forecast: 2011-2015, Gartner: 2012 “Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016
  • Big data needs a unified approach One platform for structured, semi, and unstructured to profit from 100% of data Enable me to: on Capture Store Manage Analyze Optimize 9 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 100% of data Structured warehouses CRM, transactions, sales, marketing… Universal log management IT logs, security logs, social, tweets, JSOn’s Unstructured Audio, video, emails, sentiments, threat…
  • Is it too late? • • • • • Easy to forget that it is just the first inning More than three exabytes of new data are created each day Expansion underway for more than a decade Important to not big data references more than just Google, eBay, or Amazon-sized data sets Opportunity for a company of any size to gain advantages from big data stem from data aggregation, data exhaust, and metadata — the fundamental building blocks to tomorrow’s business analytics. Combined, these data forces present an unparalleled opportunity • Despite how broadly big data is being discussed, it is still a very big mystery to many • Misunderstandings around big data seem to have reached mythical proportions 10 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top myths Big data is only about massive data volume Big data means Hadoop Open source is the only option Big data is new Big data is really difficult My RDBMS can handle it Big data is only for social media feeds and sentiment analysis Big data means unstructured data Big data is for historical reporting My current IT solutions will suffice 11 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top myths – how big is big? Stop worrying about size Even "big data" stirs up myths, with "big" being a very relative term Should we only be concerned about this when we have more data than we can manage? What is the relative position of big data and what are some of the myths around the size issue? Is there a certain threshold of petabytes that you have to get to? Or, if you're dealing with petabytes, is it not a problem until you get to exabytes? If it's not a size issue, then what? It's a trend that has happened as a result of digitizing so much more of the information that we all have already and that we all produce. Machine data, sensor data, all the social media activities, and mobile devices are all contributing to the proliferation of data 12 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top myths – Hadoop and open source Big data too varied and complex for one size-fits-all Greatest name recognition but not the only class Purpose-built to process very large quantities of semistructured data Mostly open source, runs on low-cost server hardware Other two options are NoSQL and Massively Parallel Processing (MPP) data stores Hadoop includes large number of components - consider that some components can be replaced to better address a need Focus on need for large-scale distributed data storage, analysis and retrieval tasks 13 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top myths – continued Main cause is likely to be the disconnect between the technical side and business value of big data Big data is new Big data is really difficult My RDBMS can handle it Three “V’s” of big data originally posited by Gartner’s Doug Laney in a 2001 • Not if the data set is handled correctly – with proper programming • Focus more on the analytics for addressing a business problem • Right people with the right tools • Good for problems they were meant to solve • Many problems today don’t need relational capability, twophase commits, complex transactions etc. • Not an either-or, typically an addition 14 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top myths – continued Modern onslaught of data that could generate economic value if properly utilized Big data is only for social media feeds and sentiment analysis • Early adapters • Opportunity for virtually every vertical • Begins with the business problem or need 15 Big data means unstructured data • Imprecise and doesn’t account for the many varying and subtle structures • Different data types within same set • Multi-structured better term • Data model applied at time of analysis © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top myths – continued Most important aspect of big data is the analytics, not just a data storage problem that’s being solved Big data is for historical reporting My current IT solutions will suffice • Depends of definition of historical • Requirement to look at it faster and to make decisions faster • Needs a combination of technology, skilled people and sufficient data sets • BI and Big Data are merging • Not just reports, dashboards and graphs 16 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Big analytics Big data analytics BI Big data BI Reactive Analytic capability Proactive Big data and BI – fitting together Large data 17 Data size Big data © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Source: SAS
  • “If you have people in the loop, it’s not real time. Most people take a second or two to react, and that’s plenty of time for a traditional transactional system to handle input and output. That doesn’t mean that developers have abandoned the quest for speed.” Joe Hellerstein, Chancellor’s Professor of Computer Science at UC Berkeley © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Top truths Volume, velocity and variety Big data is immature and lacks tools Security and data governance are overlooked – lack of stewardship Big data is not for real time Skilled, experienced staff difficult to find Frustrations– not objective, not impartial and not anonymous 19 Big data is difficult No choice to opt out © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Evolution of big data Data size and complexity Focus areas Data generation and storage Data utilization Structured data Very complex, unstructured Unstructured data Multimedia Relational databases Data-intensive applications Complex relational Primitive and structured Data driven Mainframes Basic data storage Pre-relational (1970s and before) Exponential growth in data volume Relational (1980s and 1990s) Relational+ (2000s and beyond) Computing timeline 20 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Source: A.T.Kearney
  • What to do? Net findings • Big data is a reality and an opportunity • Either you or your competition is taking advantage of it • Enough momentum to add business value Suggested steps • Pick a project that's going to address a business issue that you've been unable to address in the past • Identify questions that need to be answered to move forward – cost reduction, new markets, customer behavior discovery, suspect activity? Don’t start with the technology layer IT and business owners need to work together 21 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • HAVEn – big data platform HAVEn Hadoop/ HDFS Process and index all information Social media 22 Video Audio Email Texts Enterprise Analyze at extreme scale in real-time IDOL Catalog massive volumes of distributed data Vertica Autonomy Collect and unify machine data Mobile Security Transactional data © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Documents IT/OT nApps Powering HP Software and your apps Search engine Images hp.com/haven
  • For more information Attend these sessions Visit these demos • Big Data – Tools and Tricks • Autonomy – ESM Data Leak Demo • Jeremy Kelley • Booth Area • Session Id 1324 • Demo number 23434 After the event • Contact your sales rep • Visit the www.hp.com/haven • Download the whitepaper at: www.hp.com/whitepaperfor bigdata Your feedback is important to us. Please take a few minutes to complete the session survey. 23 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Thank you © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.