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Hadoop within the data center of the future
 

Hadoop within the data center of the future

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Big Data technologies are surfacing in data centers to solve problems legacy systems were not built to handle. Hadoop is one of those technologies. Successful Hadoop implementations share common ...

Big Data technologies are surfacing in data centers to solve problems legacy systems were not built to handle. Hadoop is one of those technologies. Successful Hadoop implementations share common characteristics and also address the requirements needed to function as a proper tenant within the data center. This session addresses those common characteristics as well as the integration requirements that need to be addressed for Hadoop within the data center of the future.

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    Hadoop within the data center of the future Hadoop within the data center of the future Presentation Transcript

    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Hadoopinthedatacenter Donald Livengood/ June 2013
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.3 My background Title Distinguished Technologist TS Consulting IT industry experience • Big Data • Client Infrastructure, Mobility, VDI • Unified Communications & Collaboration • Virtualization & Private Cloud • Electronic Messaging & Directory Services Professional information • Certified Infrastructure Architect Years at HP 28 Current responsibilities Responsible for the creation of services and delivery readiness for Big Data Infrastructure world-wide Name: Donald Livengood E-mail: djl@hp.com
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.4 Agenda Big Data Why Hadoop exists Designing Hadoop Integrating Hadoop into the datacenter
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. BigData
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.6 Gartner "Big Data" is a popular term generally used to acknowledge the exponential growth, availability and use of information in the data-rich landscape of the emerging information economy era. What is “Big Data” HP definition “Big Data is a class of data challenges, due to increasing volume, velocity, variety, and complexity, that are beyond the capabilities of the traditional software, architecture, and processes to effectively manage and utilize.” What does Big Data mean for Enterprise IT? A combination of IT capabilities to deal with volume, velocity, variety of data. McKinsey Report “Big Data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. Note: Slide for internal use only
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.7 Big Data variables forcing Big Data technology adoption Variety Any type of data Volume Ability to handle very large amounts of Data Velocity Process all data quickly Voracity End-user appetite for Big Data consumption Data in many forms Structured, unstructured, text, multimedia. Relevant information are into unstructured data. Data consumption Ingestion and processing of Data Real Time Processing. Velocity as it relates to consumption of big amounts of data Data quantity Scale from terabytes to petabytes to zettabytes. Volumes that traditional Data Management technologies cannot handle in time for consumption Data creation & transport Streaming data, milliseconds to seconds to respond. Velocity related to ingestion, cleaning, meaning of data.
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.8 Traditional Information Technologies are not adequate with Big Data SQL Consistency Availability Big Data Traditional Finding out useful information requires powerful analytics and massive processing Variety Volume Velocity Real-time data processing (vertical DB, In-Memory DB) Scale-Out, Partitioned architecture Handle Structured and Unstructured Data Voracity Allow for multiple Ingress points (Query, Search)
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.9 Agenda Big Data Why Hadoop exists Designing Hadoop Integrating Hadoop into the datacenter
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. WhyHadoopexists
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.11 StoragePlatform Today's architecture Classic ETL Processing Business Transactions and InteractionsBusiness transactions and interactions CRM – ERM – SCM FMS – HRM $ € ¥ Transaction Data Analytical, Dashboards, Reports, Visualization Enterprise Data Warehouse Business intelligence & analytics
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.12 StorageOnlyplatform(SAN/NAS) Gap in today's architecture Social media data Forum Blog Feeds Web Clicks Multi- media Audio Video Images Document management Content Management File Sharing File Hosting Collaboration Search Message data IM and VOIP Messaging System Sensors data GPS Sensors devices RFID Other events Classic ETL Processing Business Transactions and InteractionsBusiness transactions and interactions CRM – ERM – SCM FMS – HRM $ € ¥ Transaction Data Analytical, Dashboards, Reports, Visualization Enterprise Data Warehouse Business intelligence & analytics Moving data to compute doesn’t scale Can’t explore original data Archiving = Death Cheap storage Expensive restore Data dropped due to ETL Can’t handle data types Schema change takes time
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.13 Hadoop Gap in today's architecture Social media data Forum Blog Feeds Web Clicks Multi- media Audio Video Images Document management Content Management File Sharing File Hosting Collaboration Search Message data IM and VOIP Messaging System Sensors data GPS Sensors devices RFID Other events Classic ETL Processing Business Transactions and InteractionsBusiness transactions and interactions CRM – ERM – SCM FMS – HRM $ € ¥ Transaction Data Analytical, Dashboards, Reports, Visualization Enterprise Data Warehouse Business intelligence & analytics Move some data to legacy system All data available Keep Data in Hadoop Cheap storage & can tier Always available Hadoop Use MapReduce
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.14 What is Hadoop? Hadoop consists of two core components • The Hadoop Distributed File System (HDFS) • MapReduce - Computation Framework (engine) - Resource Manager & Scheduler - Other engines are/will be introduced (Impala) A set of machines running HDFS and MapReduce is known as a Hadoop Cluster • Individual machines are known as nodes • A cluster can have as few as one node, as many as several thousands • More nodes = better performance! There are many other projects based around core Hadoop The ‘Hadoop Ecosystem’ includes many projects eg, Pig, Hive, HBase, Flume, Oozie, Sqoop, etc A flexible and scalable architecture for large scale processing and computation across a distributed network of computers
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.15 Nodes & roles Master Nodes: Name Node • Oversees data storage in HDFS – Maps a HDFS file name to set of blocks, maps blocks to DataNodes Job Tracker • Coordinates parallel processing using MapReduce Slave Nodes: DataNode (slave to Name node) • Block server – Stores blocks as separate files on local filesystem • Communicates to NameNode re: existing blocks TaskTracker (slave to Job Tracker) • Starts and monitors Map tasks • Heartbeat and status to Job Tracker Edge Node - Not part of Hadoop architecture - Usually not part of cluster (but could be) - 1 or more used for ingress/egress to/from cluster - Provides authenticated users with access to private subnet (cluster) - Configured for transient storage & high bandwidth to core network
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.16 Hadoop HDFS and MapReduceBlock 2 Block 3 Block 4 Block 5 Block 6 Block 1 Server 1 Server 2 Block 1 Block 2 Block 1 Block 3 Server 3 Block 5 Block 6 Server 4 Block 2 Block 3 Server 5 Block 4 Block 5 Server 6 Block 4 Block 6 HDFS MapReduce Mapping Process Shuffle Data Reduce Process Outputs Stored locally to HDFS
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.17 Agenda Big Data Why Hadoop exists Designing Hadoop Integrating Hadoop into the datacenter
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. DesigningHadoop
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.19 HP Reference Architectures provide a firm baseline for a balanced cluster Hadoop Sizing: Workload Matters Examples IO-bound workloads • Indexing • Searching • Grouping • Decoding/decompressing • Data importing and exporting Computation Optimized Low Power Consumption Balanced Balanced/ More Power per Node Storage Optimized Fewer Disks Disk More disks LowCPUHigh Examples CPU-bound workloads • Machine learning • Complex text mining • Natural language processing • Feature extraction
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.20 Caveat: You must know your workload Workload-based Configuration Approach (1 of 2) Base guidelines NameNode: RAID1, 32GB per 1M files, 4+ disks (usually 64GB balanced) Datanode: 1GB per core, 1 disk per core - 4 1TB or 2TB hard disks in a JBOD (Just a Bunch Of Disks) configuration - 2 quad core CPUs, running at least 2-2.5GHz - 16-24GBs of RAM (24-32GBs if you’re considering Hbase) Network: - 1Gb Ethernet for nodes, 10Gb for edge nodes and network switch uplinks - Use 10Gb if “free”: can drive cost very high for adapters and switches
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.21 Caveat: You must know your workload Workload-based Configuration Approach (2 of 2) Light Processing Configuration: 1GB per core - (1U/machine): Two quad core CPUs, 8GB memory, and 4 disks - CPU-intensive: Use 2GB per core versus 1GB Balanced Compute Configuration: 2 to 3GB per core - (1U/machine): Two quad core CPUs, 16 to 24GB memory, and 4 disks Storage Heavy Configuration: 2 to 3GB per core, big storage & power - (2U/machine): Two quad core CPUs, 16 to 24GB memory, and 12 disk drives - Power consumption ~200W in idle state and can go as high as ~350W when active Compute Intensive Configuration: large memory, moderate storage - (2U/machine): Two quad core CPUs, 48-72GB memory, and 8 disks - Used when a combination of large in-memory models and heavy reference data caching is required.
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.22 Which platform to choose? DC rack capacity is limited DC cooling & power are issues High density commodity servers Need to balance • Core: disk ratio (1:1) – threads help • CPU cost – power budget and price • Disk capacity – more is better Average size ~ 20 servers Plan for change! Optimizing Rack Capacity DL360p SFF(12 core), 1.20 DL380p/e(16 core), 1.33 SL4540(16 core), 1.07 DL380p/e(12 core), 1.00 350 400 450 500 550 600 650 400 500 600 700 HardDrives/Rack Cores/Rack Hadoop Data Nodes Core/Disk Ratios per 42u Rack DL360p SFF(12 core) DL380p/e(16 core) SL4540(16 core) DL380p/e(12 core)
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.23 Item %Cost Memory 40% Disk 36% Chassis 8% Network 7% CPU 6% Software 2% Rack 1% Cost Distribution – SL/DL Server rack Network Load balanced, redundant, wire-speed Separate management network Chassis/CPU DL/SL series of commodity servers Single quad-core, mid-range Xeon Disks Full complement of LFF Terabyte disks Memory (24)32 GB of ECC Disk and Memory are the largest cost contributors 23
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.24 Hadoop Physical Architecture Typically organized as racks of commodity servers with DAS storage “Commodity” Server Hardware 1GbE Rack Switches ECC Memory Storage using SATA disk Only master servers require RAID disk Out-of-band management via iLO Rack | HPN Top of Rack Switches Management Node Hadoop Master Hadoop Slave Hadoop Slave Rack | HPN Top of Rack Switches Hadoop Slave Hadoop Slave Hadoop Slave Hadoop Slave Cluster Switch Rack | HPN Top of Rack Switches Hadoop Slave Hadoop Slave Hadoop Slave Hadoop Slave iLO1Gb iLO1Gb iLO1Gb 10Gb 10Gb
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.25 Hadoop Solution Packaging Consistent with Reference Architectures & Hadoop Appliance 0 | HPN Top of Rack Switches Management Node Hadoop Master Hadoop Slave Hadoop Slave Head Rack Enclosure | HPN Top of Rack Switches Hadoop Slave Hadoop Slave Hadoop Slave Hadoop Slave 42u Rack Expansion | HPN Top of Rack Switches Hadoop Slave Hadoop Slave Hadoop Slave Hadoop Slave 42u Rack Expansion Base Expansion
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. DesigningHadoop:Network
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.27 Network considerations Hadoop is a high-performance computing platform - Hadoop drives performance and availability through IP communications Guidelines - Cluster must have dedicated switching – no shared switches or VLANs: • Network traffic characteristics of Hadoop demand this - All servers should use 1Gb/10Gb Ethernet to the Top of Rack (ToR) switches - All ToR switches should have multiple 10 GbE connections to the core switches, for both bandwidth and redundancy • Integrated Lights Out (ILO) management may be supported from a separate 1GbE/100 Mbps network Use server bonded NICs and redundant ToR switches - Cost is higher but worth it in multi-rack clusters - Improved bandwidth - Avoids replications costs on failure of ToR switch - Connect ToR to Aggregation switches to join racks - More complex but significant benefits • HP can provide assistance
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.28 Network considerations Routing • Cluster should not route any in-cluster traffic out of the cluster − Misconfigured routers can allow this Network & port stress • Hadoop can stress all ports, across all servers and ToR switches for extended periods − Use switches suited for Hadoop, not just “favored” switch types − Network traffic characteristics of Hadoop demand this DNS • Hadoop makes many DNS & reverse DNS lookups − Even for nodes within the cluster • Use and maintain local /etc/hosts file for in- cluster lookups • MapReduce jobs making excessive calls to remote servers can general large amounts of external traffic • May consider placing cached DNS server in every worker node to mitigate the problem Integration into the corporate network
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. “We’veprofiledourHadoop applicationssoweknowwhattype ofinfrastructureweneed” Saidno-one.Ever.* *Credit: HP Hadoop engineer
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.30 Balanced design approach Whole Cluster as an Appliance Well defined Ingress/Egress Interfaces Cluster Deployment & Management Integration into DC Monitoring Infrastructure-isolated cluster network • Simplifies cluster network • Separates cluster traffic load • High speed connections for ingress/egress DMZ Edge Nodes Appliance Cluster Access via controlled interfaces to minimize disruption, improve security, and reduce risk to DC and processes Data import Data export Monitoring Management
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.31 Enterprise-ready Big Data platform Pre-integrated, pre-tested, pre-engineered We’ve done all the hard work for you Full-rack, half-rack, expansion rack options Out of the box Not in months, but hours or days Super fast Loading, sorting, and analysis Easy scaling Expansion racks available Via CMU: 800 nodes in 30 minutes HP AppSystem for Apache Hadoop
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.32 Deploy …it’s like using as-is open source technology, you have a lot of work to do! Without AppSystem Without the HP AppSystems ~ 8+ weeks Research components Develop complex Design Order collection of parts Assemble parts Install, upgrade firmware & software Test & adjust design Find your mistakes somewhere in here and start over With HP AppSystem for Apache Hadoop ~ 4 weeks Choose your AppSystem Order the AppSystem Installation Deploy Success!
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.33 Agenda Big Data Why Hadoop exists Designing Hadoop Integrating Hadoop into the datacenter
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Integrationintothedatacenter
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.35 Big Data Transformation Big Data information refinery Insight Processing Protection & Compliance Management Infrastructure Integration Enterprise Data Warehouse Analytical, Dashboards Reports, Visualization Business intelligence Business transactions and interactions Web, Mobile CRM – ERM – SCM FMS – HRMValue Creation share, refine & development Message data Document management Social media data Multi-media Sensors data
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.36 Big Data Functional Architecture: a refinery approach Technology Integration Security Operations Big Data Converged, Automated, Energy efficient infrastructure Activity logging Intrusion Prevention Switch virtualization Virtual application network SSL/VPN Networks Storage replication Server scale-out management Collection Computation Consumption Protection Big Data Management Compliance Big Data Storage Big Data Processing On Line / Batch Analysis Internal / External Data Structured / Unstructured Real Time Backup and Recovery Governance Privacy and Security Destruction Archival Retention
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.37 Functionalities Destruction Backup and Recovery Governance Privacy and Security Protection Compliance Retention Archival Protection qualities Confidentiality, integrity, availability De-duplicationReplication Data quality metrics Compliance qualities Rapid search (Legal) Tiering
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.38 Big Data Classification ! Classification Retention period Recovery Time Objective (RTO) Recovery Point Objective (RPO) Forensic window Vital / Critical 7 years 30 minutes <10 minutes 6 months Sensitive 5 years 1 day < 1 hour 3 months Non critical 6 months 1 week < 48 hours 1 month
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.39 Confidentiality, Integrity, and Security of Big Data Big Data Security Unique Big Data Threats Data Privacy Preservation CSIRT Program Changes Security Controls Security Technology eDiscovery !
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.40 Security integration Confidentiality Identity Access Identity Access Perimeter Security Confidentiality Perimeter Security Refinery Outbound Presentation Refinery Inbound Pipeline
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.41 Big Data Security examples HP related technologies HP ProtectTool • Authentication Services • Multi-Factor Authentication • Role Based Access (RBAC) HP TippingPoint IPS • In-line protection • Real-time threat protection Qualities Role based Speed Reliable Flexible authentication Perimeter Security Speed Reliable Managed Real-time Identity Access
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.42 Backup and recovery Backup and Recovery Policy target time Deduplication eDiscovery Vaulting Replication Media transfer performance Storing reliability Qualities HP ESL Tape Backup StoreOnce • Back up up to 100TB/hr with Catalyst • Restores up to 40TB/hr • Couplet redundant • Tape vaulting HP Related Technologies
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.43 Governance Variety Velocity Voracity Volume Validity Accuracy assurance Consistency assurance Accessibility assuranceBig Data Governance
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.44 Privacy & Security HPRelated Technologies ArcSight Security Intelligence • Threat Detection • Security Analysis • Different data sources log data management • Legal and Compliance Qualities Confidentiality Connectors Compliance Traceability Speed Autonomy Security Performance Suite • Data Protector • Live Vaulting • eDiscovery • Compliance Archiving • Records Mgmt Protection Vaulting Fast Restore Encryption Fast Discover Security Controls Data Privacy Preservation eDiscovery Privacy & Security Policy
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.45 Protection Purging Shredding Wiping Degaussing Different types of media Protection Policy
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.46 Archival HP Related Technologies 3PAR StoreServ StoreAll • Console based tiering • Express Query and Autonomy IDOL integration • Mesh-Active Architecture • Thin technologies • Peer Motion • Virtual Lock • Adaptive Optimization Qualities Automated policy- based tiering Rapid search Extreme Data Reduction Scalable Storage Archival Policy
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.47 Retention Sarbanes-Oxley HIPAA PCI DSS Safe Harbor Data Privacy Act GLBA HP Related Technologies 3PAR StoreServ StoreAll • Console based tiering • Snapshots and data validation • WORM features • File and Object Storage • 16PB namespace Qualities High scalability Automated policy- based tiering Data Protection WORM potentiality Open standards interface Retention Policy
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.48 Enabling Big Data with Networking & Storage example HP Related Technologies HP FlexNetwork Architecture • FlexCampus • FlexFabric • FlexManagement HP Converged Storage • HP 3Par StoreServ • HP StoreAll • WAN Optimization HP Infrastructure Tools • Insight CMU • IMC • StoreVirtual software Qualities Simplicity Speed Scalability Identity-based access Storage Geographic Snapshot and Cloning Capabilities Thin Provisioning Seamlessly handle fast moving data Network IT Operations Manageability of: Connections Storage Scale-out Server Scale-out
    • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.49 End-to-End Service level Big Data Refinery Service Level Target: Current different Service Level: Business transactions and interactions Very High  High  Business intelligence & analytics High  Message data High  Document management Medium  Multi-media Very Low  Sensors data Medium  Refinery's consolidated Service Level: Low  Social media data Big Data information refinery Insight Processing Infrastructure Integration Management
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.50 Summary slide Do you know the technology you will use and the workload? • If not, check out HP AppSystems and Reference Architectures for Hadoop (and other Big Data technologies) Skills • Do you have experience with high-performance Linux clusters or Hadoop clusters? Space & power • Can your data center handle the space, power, and cooling now & in the future? Network & storage capacity • Can they handle data movement, staging, post-processing, and export/import? • What load (export/import) can existing BI/Analytics systems handle? Monitoring & Support framework • How will Hadoop ecosystem integrate IT architectural requirements & standards
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.51 For more information Attend these sessions • RT3462, Big Data Analytics 360 • RT3463, Big Data & the internet of things • TB2590,What’s new in HP Vertica • BB3378, Any data, any size • TK2789, Keynote: Make information matter Visit these demos • HP AppSystem for Apache Hadoop • IT Big Data Transformation Experience After the event • Contact your sales rep! • Visit www.hp.com/go/bigdata • Visit www.hp.com/go/hadoop Your feedbackis important to us. Please take a few minutes to complete the session survey.
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.52 Learn more about this topic Use HP Autonomy’s Augmented Reality (AR) to access more content 1. Launch the HP Autonomy AR app* 2. View this slide through the app 3. Unlock additional information! *Available on the App Store and Google Play
    • © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Thankyou