VCE EMC Julianna DeLua Ryan "THE I silon guy" Peterson Richard Villalobos Claudio Fahey

1. Weekly Sales Enablement CallFebruary 3, Monday - 3pm PTFebruary 5, Wednesday - 8am PTBig Data & Analytics VCEEMC Julianna DeLua Ryan "THE Isilon guy" Peterson Richard Villalobos Claudio Fahey Mark Hedley February 2014

2. Did you know 76% of VCE customers are currently using (35%) or considering using (41%) Vblock for big dataand Analytics in 2013?

3. Why discuss big data? What’s in it for you? Increasing business pains with big data Solution: big data and analytics on Vblock Hadoop and Isilon Sizing discussion Resources Agenda

4. Become more strategic by helping your customer rethink infrastructure From dev / QA to production Mission-critical second/third gen architecture Address “ variety” in addition to “volume” and velocity” Respond to request effectively Hadoop opportunities on the rise Increased leads from EMC, Cisco and Pivotal ramping up effort Help you win existing deals Show your understanding of market transition Differentiate against competitions Why address big Data now?What is in it for you?

5. Extend our leadership in Converged infrastructure to bring Big Data to core VCE in a prime position to transform business with Big Data • The following products/services are NOT OPEN for bidding in this RFP: • Products • Any technology that clashes with a current bank strategic platform (MPP database for example) • Commodity or Appliance Hardware • Source: Request for Proposal - Global 100 Bank, December 13, 2013 2013 is the year of larger scale adoption of big data. 42% of respondents invested in big data or were planning to do so within a year.(Source: Doug Laney, “Predicts 2013 information innovation March 13) Industrial Internet Will Add $15 Trillion to Global GDP by 2030(Source: GE April 13) Source: McKinsey, Game changers: Five opportunities for US growth and renewal, July 2013

6. Challenges that can not be solved by commodity infrastructure • Unable to impact business with siloed“big data” projects and shadow IT, away from enterprise IT • Lack of flexibility in moving to IT-as-a-service – virtualization? Multi-tenancy? • Security and privacy concerns in commodity infrastructures, cannot run as a service • Availability and scalability concerns moving to production environment • Not getting value from our big data initiative with limited maturity • Unable to decouple server/storage and scale tuned to business demand The Garter survey based on 720 members worldwide found that of the64 percent of organizations investing or planning to invest in big data technology in 2013, 30 percent have already invested in big data technology, 19 percent plan to invest within the next year, and an additional 15 percent plan to invest within two years. (Source: Gartner September 2013)

7. Converging your investments to drive value from big data Hadoop Big data into core business Evolve analytics with big data for high value High Availability and Performance Pre-engineered system with best-of-breed technologies As-a-Service operation Virtualization, multi-tenancy, back-up recovery and business continuity Standardization and flexibility Standardized across data centers, adaptable to logical and physical change Private Public Incremental deployment Ease of scaling compute and storage and mix and match technology components Security, governance, and compliance Oversight and visibility over disparate data sources Next Generation of IT New Approach: Embrace existing and new data and applications on an open, converged platform

8. Target use cases to combine All Data Horizontal Risk and Fraud Management Customer & Offer Management Logistics & Operations Process Efficiency Price Optimization Vertical Financial Services Retail/Online Services Healthcare Oil, Gas, & Energy Telcos Unstructured data – logs, POS, images, video/audio, locational, network genomics, email, docs, etc. Structured data – customer, patient, product, supplier, purchase, financial, risk, etc. Multi/structured, Semi-structured or industry standards (HIPAA, HL7, NCPDP, ACORD, DTCC, EDI-X12, EDIFACT, SWIFT, FIX, NACHA)

9. DAS = direct attached storage Styles of Big Data & Analytics on Vblock HDFS = Hadoop distributed file systems

10. Wait a minute, is it that easy?

11. Objection Handling expected • Hadoop is built for commodity servers! • Hadoop, especially HDFS 2.0 is for enterprise and supporting infrastructure • I read in the O’ Reilly book that we need x3 to x5 replication for throughput and protection • Isilon overcomes BOTH performance with scale-out nodes and enterprise class protection • The internal drive can be slower. End-to-end data management process adds overhead • DAS is the only way to drive performance, keep storage close to CPU • There is some difference yet the benefits of virtualized Hadoop outweighs small penalties. VMware benchmarks available • Virtualized Hadoop is too slow • Does not big data need dedicated appliance? • Cisco/EMC hardware advances in make best-of-breed solutions big data ready

12. Isilon and Hadoop

13. HDFS: INTEGRATED ISILON WITH HADOOP Web Click data Hadoop Cluster NFS MAP Reduce node info Step 2: Jobs are run MAP Reduce node info Decision Support Databases MAP Reduce node info MAP Reduce node info SMB, NFS, HTTP, FTP, HDFS OLAP data node name node name node name node name node Step 1: Much or all of the Data lives on the Isilon/Hadoop Cluster Isilon EDW

14. Independent Scaling Before • Storage to Compute ratio is fixed • Scaling compute means scaling capacity • Difficult to provide QoS • Compute upgrade is a forklift Required performance/capacity • After Storage Required Hadoop Cluster Nodes • Scale compute independent of storage • Achieve optimal performance balance even as workloads evolve • No data migrations, ever! • Add new performance as hardware evolves Compute

15. Snapshot/Version Control Before • Traditional HDFS does not have replication • No Snapshotting of data • Loss of Version control • Not designed for Mission Critical   • After • Full Snapshot IQTM integration identifies changes • Multi-threaded, Multi-Node Scale-Out replication • Improved RPO/RTO for business continuity • Geo-replicated Hadoop!

16. VIRTUALIZE HADOOP ON VBLOCK Drive Operational Simplicity with Performance Maximize Resource Utilization Architect Scalable Platform Node reply Node reply Node reply Node reply PIVOTAL Hortonworks NFS SMB Virtualization SMB, NFS, HTTP, FTP, HDFS SMB name node HDFS data node name node NFS name node name node Isilon name node vSphere Big Data Extension VMAX / VNX name node name node Apache

17. Cost Comparison: DAS vs Isilon Customer requirements • 640 TB raw capacity DAS Option • 14.8% usable capacity/DataNode • 38 racks of servers Isilon Option • 10 Racks (including Compute) • 65% less expensive than DAS $ 6M Network HadoopLicensing $ 5M Management $ 4M Config Installation $ 3M Energy Isilon $ 2M Servers $ 1M $ 0 Hadoop on Isilon is often significantly less costly!

18. Why HDFS on Isilon with Vblock • No Ingest necessary • Eliminate NameNode SPOF • Eliminate 3x mirroring • Enterprise feature set • Multi-protocol access • Simultaneous Multi-distribution support • Better cost! • SmartDedupe for Hadoop • SEC 17a-4 Compliant WORM • Kerberos Authentication • Hadoop Multi-tenancy • Supports All Major Distros—Apache, Pivotal, Hortonworks, Cloudera • Great performance! Get HDFS license key included in One FS – no additional license cost • New Way Forward: Deploying Hadoop on Vblock with Isilon as a shared infrastructure

19. Sizing and Technical Considerations

20. SAME Sizing outcome you Look for in Big Data and Analytics virtualization • vSphere, including Big Data Extension (BDE) for Hadoop • Volume – higher transaction volume, change data, # of queries, etc. • Velocity – lower response time, latency, and coordination of batch/real-time • Variety – data types, block/files. eg. XML behaves differently from image Server • B-Series (baseline) or C-Series • # of nodes, CPU, Memory, Disk Network • Confirm no customization necessary Storage • EMC Symmetric VMAX, VNX and Isilon • Raw Storage Capacity Required • EMC Avamar, Data Domain, VPLEX, RecoverPoint • RPO, RTO, Distance and Volume Protection KEY – Pay attention to workloads (visualization/analytic apps, MPP databases, Hadoop, HDFS, HBase, name/data nodes, etc. ) and translate metrics into sizing

21. If virtualized : Take CPU requirement and convert it to spec int.  Provide enough processors to serve total spec int requirement Calculate nodes per blade based on CPU requirement  Calculate memory per blade based on nodes per blade(ensure division provides whole number of node memory) Use 5400 or 5600 VNX array if blade limit doesn't impact the solution Provide boot space for ESXi Provide space for local disk on node in VNX Attach isilonconfig as parent quote Ensure additional nexus 5k, and enough twin ax cables are added Ensure enough physical ports are provided on the Vblock nexus 5k for uplinks from isilon 5k Size DR solution based using standard methods, ensure that ports used for DR aren't consumed by isilon uplinks If not virtualized Provide blades to match CPU, and memory requirement (blades may provide too much CPU) Configure the remainder with notes from virtualization Considerations for Big Data Workload • Review whitepaper on Isilon attach to 300/700 • Contact EMC isilon SE to assist with  holding isilonconfig

22. Adding Isilon and Pivotal CF to your VCE BOM

23. https://sales.vce.com/products/vce-select/vce-select Ordering • HOW to add Isilon and Pivotal to a VCE BOM? Follow the VCE Custom add process. • VCE sales rep to contact Investor company rep for a quote. • EMC Account Team and/or Isilon rep for Isilon • Pivotal Account Team for Pivotal CF • Work with Deal Desk to have your quote added to your opportunity and follow the steps highlighted in the RED Box. Any questions contact Deal Desk • Note: When requesting an Isilon or Pivotal CF Quote be sure to specify if the quote is for a Partner or Direct sale. Choose New EMC Isilon Product Field in SFDC to track Isilon quote in your Opportunity

24. CONCLUSION and NEXT STEPS • In summary • Technology inflection point is here – opportune time to engage with your key accounts on big data and analytics • VCE is invited to more opportunities in 2014 – investors see us VERY strategic • We are here to help you succeed • Calls to Action • For sales, complete “Vblock with a touch of Isilon” accreditation • For vArchitects, check out Isilon Hadoop solution selling • Pick 1-3 accounts to target and apply what you learned

25. 2014 Big Data and Analytic Initiatives • Big Data and Analytics Assets – White Paper, Solution Brief and Assets • Pivotal Onestop Shop • http://wiki.ent.vce.com/display/AO/Pivotal • Big data and HadoopOnestop Shop • http://wiki.ent.vce.com/display/AO/Big+Data+and+Analytics%2C+Hadoop • What’s Next • Hadoop White Paper in Q114 • EMC Federation and Cisco Eco-System Support • Build out Big Data and Analytics Eco-System • Cisco Validated Design (CVD) with EMC and VCE • VB340 plus Isilon with virtual Hadoop • Timed with EMC world and Cisco Live in May • Big Data and Analytics Champions in regions • Contact • Products: julianna.delua@vce.com • Partners: pete.hammack@vce.com • Isilon: carl.washington@emc.com • Pivotal: cf-sales-help@gopivotal.com GO BIG

27. Backup

28. Isilon OneFS: Built for Big Data Massive Scalability • Up to 20PB in a single file systems • Unmatched Performance • Up to 118 GB/s of concurrent throughput • Up to 740 MB/s single stream throughput • Up to 25.1 TB Global Cache Application and Workflow Consolidation Industry-leading Reliability and Self-Healing Management Simplicity – automates activities “unfit for humans” • Symmetric scale-out architecture • Fully distributed, fine-grained services • Unified IP storage (NFS, SMB, Object, HDFS)

29. Why Customers interested in running big data and analytics • Virtualization • VblockSystems Run multi-tenant service model Speed Deployment Rationalize HW/SW Costs Aggregate Workloads Increase Utilization Optimize and Standardize Infrastructure Provision Faster Lower Operational Cost Improve Quality of Service and Availability Secure Environment Promote Innovation Pre-Engineered,Pre-Validated, Pretested

30. Embrace All Data on a converged Platform for Big data and analytics virtualization VMware vSphere including Big Data Extension (BDE) Server Cisco Unified Computing System (UCS) servers Cisco Data Center and Cloud Networking (DCN) portfolio Network Storage EMC Symmetric VMAX, VNX and Isilon Protection EMC Avamar, Data Domain, VPLEX, RecoverPoint

31. It being fundamentally disrupted MOBILITY CLOUD BIG DATA New Applications Speed Economics Internet of things Team Skills Complexity

32. VCE customer benefits • Results of September 2013 IDC research study • 4X FASTER TIME TO DEPLOYMENT • 5X FASTER TIME FORNEW SERVICES • 25 days down to 5 days • 160 days down to 40 • 79% less staff effort • 96% REDUCTION • IN DOWN TIME • 50% REDUCTION • OF ANNUAL DATA CENTER COSTS

33. Vblock Systems • True Converged infrastructure SOLUTIONS & SERVICES VCE & PARTNERS SYSTEM720 SYSTEM100 SYSTEM200 SYSTEM340 SPECIALIZEDSYSTEMS VCE Vision™ Intelligent Operations software

34. Customers run Big Data and Analytics with mixed workloads on Vblock Vblock System for Databases Use Cases (n=171) Vblock System for Private Cloud Use Cases (n=172) Vblock System for App Dev/Test Use Cases (n=158) Vblock System for Microsoft Suite Use Cases (n=156) Vblock System for SAP/ERP Use Cases (n=130) Vblock System for Business Continuity/Disaster Recovery Use Cases (n=160) Vblock System for VDI Use Cases (n=160) Vblock System for Big Data/Analytics Use Cases (n=118) Vblock System for Remote/Branch Office Use Cases (n=128) Source: VCE 2013 Customer Satisfaction & Loyalty Survey

35. Big Data Use Case By Industry

36. DAS = direct attached storage FEX = Fabric extender VCE Distinct Benefits to Business ✔ ✔ ✔ ✔ ✔ ✔

37. DAS = direct attached storage FEX = Fabric extender VCE for Virtual shared infrastructure GAP

38. Example Solution Architecture Note: Detailed architecture would vary based on sizing and customer environments

39. Virtualized Hadoop - vSphere Big Data Extensions Value Propositions Operational Simplicity with Performance Maximize Resource Utilization Architect Scalable Platform • Rapid Deployment • Self service tools • Performance • True multi-tenancy • Elastic scaling • Avoid dedicated hardware • VM-based isolation • Increase resource utilization • Deployment choice • Maintain management flexibility at scale • Control Costs • Leverage vSphere features

40. VBLOCK™ System evolving to accommodate big datA and analytics • Workload patterns change over time • Shifting resources need flexible points of scalability • Simplified deployment, management,and ongoing operations MODULAR SCALABLE Designed for the next gen data center

41. Integrated data protection Option VBLOCKTM DATA PROTECTION • Continuous availability, best in class system uptime • Advanced de-duplication • Optimum resource utilization • Eliminate risk, faster time to value • Support for entire Vblock Systems portfolio Avamar 7 plus Data Domain EMC RecoverPoint EMC VPLEX

42. VBLOCK™ extended to drive value from big data and analytics Architected to drive value from shared infrastructure model Virtualized for multi-tenancy, bare metal also available Converge data and application investments incrementally • Lifecycle System Assurance • Integrated data protection options • Lower TCO – operations, process, and licensing • Mission critical availability • Mixed workload capable • Hadoop ready - integration of HDSF with Isilon • Reuse existing skills for Vblock 300 and 700 family

43. Solution leverages Current Vblock Big Data and Analytics on Vblock http://media.vceview.com/documents/isilon-with-vblock-300-700-deployment.pdf Open to Hadoop Distributions Applications/Systems VMware vSphere inc. Big Data Extension (Serengeti) for virtual provisioning Server Flash - XtremSF/SW (Optional, Phase 2) Server Cisco B- or C-series Network Cisco unified networks VB340 or VB720 • Virtual, Shared infrastructure to Support Mission-Critical Deployment VNX (or VMAX) Pivotal DB/Structured Data Boot/Virtualized LUN Isilon Native HDFS integration Base configuration Isilon