Hitachi Data Systems Big Data Roadmap
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Hitachi Data Systems Big Data Roadmap

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As more companies grow their business in global markets, they discover the need to capture new opportunities in a matter of days rather than months to have competitive advantage and to capture new ...

As more companies grow their business in global markets, they discover the need to capture new opportunities in a matter of days rather than months to have competitive advantage and to capture new market share. Their machines are producing terabytes of various data types — video, audio, Microsoft® SharePoint®, sensor data, Microsoft Excel® files — and leaders are searching for the right technologies to capture this data and help provide a better understanding of their business. The HDS big data product roadmap will help customers build a big data enterprise plan that ingests data faster and correlate meaningful data sets to create intelligence that’s easy to consume and helps leaders make the right business decisions. View this webcast to learn about Hitachi’s product roadmap to big data. For more information on HDS Big Data Solutions please visit: http://www.hds.com/solutions/it-strategies/big-data/?WT.ac=us_mg_sol_bigdat

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Hitachi Data Systems Big Data Roadmap Presentation Transcript

  • 1. INVENTING THE FUTURE HITACHI DATA SYSTEMS BIG DATA ROADMAP MICHAEL HAY CTO AND VP, GLOBAL SOLUTIONS STRATEGY AND DEVELOPMENT CHIEF ENGINEER, INTEGRATED PLATFORM STRATEGY @ ITPD
  • 2. As more companies grow their business in global markets, they discover the need to capture new opportunities in a matter of days rather than months to have competitive advantage and to capture new market share. Their machines are producing terabytes of various data types — video, audio, Microsoft® SharePoint®, sensor data, Microsoft Excel® files — and leaders are searching for the right technologies to capture this data and help provide a better understanding of their business. The HDS big data product roadmap will help customers build a big data enterprise plan that ingests data faster and correlate meaningful data sets to create intelligence that’s easy to consume and helps leaders make the right business decisions. Join this webcast to learn about Hitachi’s product roadmap to big data. INVENTING THE FUTURE: HDS BIG DATA ROADMAP WEBTECH EDUCATIONAL SERIES
  • 3. DEEP INNOVATION RESOURCES INNOVATION BUDGET  Founded in 1910  US$118B FY11  900 subsidiaries  324,000 employees  More than 760 PhDs #38 in the 2012 FORTUNE® Global 500
  • 4. 2003 HITACHI DATA SYTEMS (HDS) PORTFOLIO
  • 5. OUR JOURNEY HDS WAS A STORAGE HARDWARE VENDOR COMPETING ON PRICE Redesigned and expanded software suite Acquisition of Archivas for content software 2011-12 2003 2007 2009 Redesign of midrange hardware, packaged as solution Launch of verticals SOFTWARE DRAGS HARDWARE IMPROVED SOFTWARE VIRTUALIZATION FILE AND CONTENT SOLUTIONS 2010 SOLUTIONS DRAG SOFTWARE Acquisitions of BlueArc, Cofio 2013 ACCELERATION
  • 6. Infrastructure  Converged solution stacks  Rapid and on-demand provisioning and deployment HDS INTEGRATED STRATEGY  HIGHER VALUE  HIGHER MARGIN  HIGHER STICKINESS Data Intelligence  Data lifecycle management  Index, search, and discover independent of application Information Analytics  Data reuse for new business  Data analytics independent of application and media INFORMATION Information Virtualization Analytics Integration Integrated Information-as-a- service Text CONTENT Content Virtualization Search, discover, repurpose Link to vertical/SI markets Content-on-demand Archiving-as-a-service INFRASTRUCTURE Data, Storage, File, Server, Network Virtualization Virtualization, mobility Integrated management Data center convergence Infrastructure and platform-as-a-service
  • 7. Life Sciences Research Location-Based Advertising One to One Marketing On-Demand Maintenance Satellite Images Every industry, every geo, companies big and small BIG DATA OPPORTUNITY IS EVERYWHERE Fraud Detection Churn Analysis Risk Analysis Sentiment Analysis One to One Marketing Geomation Farming Location-Based Advertising Oil Exploration Network Monitoring Asset Tracking On-Demand Maintenance Traffic Flow Optimization Seismic Monitoring Satellite Images Fraud Detection Churn Analysis Risk Analysis Sentiment Analysis
  • 8. CONTENTINFRASTRUCTURE IP AND STORAGE NETWORKING SYSTEMS MANAGEMENT SMART INGEST HDI | HDD-MS COMMAND SUITE UCP DIRECTOR CLOUD/OBJECT HCP UCP SELECT NAS/FILE HNAS SEARCH HDDS BLOCK/UNIFED STORAGE PLATFORMS UNIFIED COMPUTE PLATFORM PRO COMPUTE PLATFORMS INSTANCE MGMT. UCP for SAP HANA | UCP for Oracle | UCP for MS Exchange | UCP for MS SQL | UCP for VMware | Etc. OUR PORTFOLIO
  • 9. BIG DATA JOURNEY OVERALL HITACHI VISION AND STRATEGY FOR BIG DATA Extending traditional analytics with Hadoop Rich media analytics Expanded vertical solutions Advanced analytics orchestration Smart ingest (e.g. JDSU, HDI) Hadoop ref. architecture Big Data ISV ecosystem UCP for SAP HANA Infrastructure layer Content layer UCP for Oracle, Microsoft Hitachi Clinical Repository Expanded Big Data services Managing data growth High performance DB analytics Real time Metadata driven content analysis Machine data Data science mainstream adoption Image, audio, video analytics Complex data mashups TODAY EVOLVING TOMORROW Social innovation Vertical solutions Market Requirements: Mainstream Use Cases Hitachi Portfolio Big Data services Scale-out architectures
  • 10. TRENDS AND PORTFOLIO DIRECTIONS
  • 11. THE EXA-SCALE ERA IS ON ITS WAY “We are planning for 100EB systems by 2020.” Advanced Customer
  • 12. THE TECH GOLDFISH BOWL THEORY  Seems counter to rational thinking, yet if you look at human behavior we tend not to delete anything.  With all of that data now available, there is a movement contemplating how to transform unused data into an appreciating asset: Big Data!  The Hadoop people are right, but not in the way they think.  In economics, Jevons paradox (sometimes Jevons effect) is the proposition that technological progress that increases the efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource.
  • 13. WIDE AREA DATA SERVICES PLATFORM f private CORE @ SITE 2 Apps & Ingestors Object Store Hitachi Content Platform CORE @ SITE 1 HDDS/Search CORE @ SITE 3 Apps & Ingestors Scale-Up NAS Hitachi Network Attached Storage private public SMART INGEST Hitachi Data Ingestor SMART INGESTION APPLICATIONS metadata warehousing Object Store Hitachi Content Platform Scale- Up NAS Hitachi Network Attached Storage NFS File Server 3rd – Party SMART INGEST Hitachi Data Ingestor
  • 14. CONSOLIDATED RACK THE EVOLUTION OF THE STACK systemsmanagement network storage compute os/vm application DIY today 2011-2013 Beyond Converged 2014-Future RACK CENTRALIZED Converged Stacks/Offerings RACKRACK RACK CustomerOR CommonESMstack CONSOLIDATED RESTful GUI CLI
  • 15. BUT WHY TAKE THIS APPROACH?
  • 16. THE FUTURE OF BIG DATA HITACHI – BIG DATA DRIVES BIG INNOVATION Machine data is in our DNA We think more like users
  • 17. BIG DATA DRIVES BIG INNOVATION TODAY Hitachi Transportation Bullet Trains Demand based maintenance Early warning improves safety More efficient asset utilization Telemetry from seismic sensors Efficient capture of time series data Hitachi Power Power Stations Operational data from sensors Insight for fleet managers Competitive differentiation Hitachi Construction Excavators
  • 18. BIG DATA ANALYTICS – VARIETY DOMINATES RELEVANTTECHNOLOGIESRELEVANTTECHNOLOGIES
  • 19. BIG DATA ANALYTICS – ARCHITECTURES MODERN 3-TIER APPLICATION database application presentation COMPONENTS FOR FUTURE BIG DATA, ANALYTICS APPS search analytic studio kvs Complex event processing visualization dwh hive Extract, Transform, Load machine learning Graph databasemany more
  • 20. ANALYTICS ORCHESTRATION AND THE ANALYTICS STUDIO UCP Orchestration Resource management (e.g. provisioning) + Analytics Orchestration VISION (Machine readable documents to auto-deploy multi-step analytics applications) The Analytics Studio VISION (A Visio-like interface for humans to create complex multi-step analytics processes and applications.)
  • 21. DECISION ASSISTS USING EVENT PROCESSING GOAL: Help brokers recommend to clients buy/sell decisions based upon corporate social sentiment IMPLEMENTATION: Multiple technologies orchestrated in vSphere
  • 22. FOOD FOR THOUGHT
  • 23.  Granular views into network, content and subscriber experience  Move from reactive to predictive problem management  The combination of JDSU PacketPortal and Hitachi streaming data platform  Leverage Big Data class technologies for penetrating insight IN-MEMORY PREDICTIVE ANALYTICS FOR TELCO ENVIRONMENTS
  • 24. BUSINESS MICROSCOPE A home improvement store was evaluated using a human attached sensor platform and in-store sensors  Resulted in increased revenues after observations and reconfiguration of staff  Facial matching techniques derived from EMIEW2 from CCTV feeds could replace/augment sensor platforms
  • 25.  EMIEW2 developed as part of Hitachi's efforts to create a service robot with diverse communication functions that could safely coexist with humans.  The new iteration combines research being explored for Hitachi content and information layers to illustrate these technologies in action.  EMIEW2 uses both visual object detection and recognition to identify and find objects. EMIEW2 – APPLIED AUDIO AND VISUAL OBJECT RECOGNITION
  • 26. QUESTIONS AND DISCUSSION
  • 27. Cloud/Object Store ‒ Hitachi Cloud Strategy, Enabling Technologies, and Solutions, Part 1, May 21, 9 a.m. PT, noon ET ‒ Environmental Pressures are Driving an Evolution in File Storage, Part 2, May 23, 9 a.m. PT, noon ET Big Data Webcast Series continues ‒ Hitachi Data Systems Hadoop Reference Architecture, June 12, 9 a.m. PT, noon ET Check www.hds.com/webtech for:  Links to the recording, the presentation and Q&A (available next week)  Schedule and registration for upcoming WebTech sessions UPCOMING WEBTECHS
  • 28. THANK YOU