INVENTING THE FUTURE HITACHI DATA SYSTEMS BIG DATA ROADMAP MICHAEL HAY

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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

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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 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

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DEEP INNOVATION RESOURCES

INNOVATION BUDGET



Founded in 1910



US$118B FY11



900 subsidiaries



324,000 employees



More than 760 PhDs

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2003 HITACHI DATA SYTEMS (HDS)

PORTFOLIO

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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

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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

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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

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CONTENT INFRASTRUCTURE

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.

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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

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TRENDS AND

PORTFOLIO

DIRECTIONS

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THE EXA-SCALE ERA IS ON ITS WAY

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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

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WIDE AREA DATA SERVICES PLATFORM

f

private

CORE @ SITE 2

Apps & Ingestors Object Store Hitachi Content Platform CORE @ SITE 1 H D D S/ Searc h 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

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CONSOLIDATED RACK

THE EVOLUTION OF THE STACK

sy st ems manag ement network storage compute os/vm application DIY today 2011-2013 Beyond Converged 2014-Future RACK CENTRALIZED Converged Stacks/Offerings RACK RACK RACK C ust om er O R C om m on E S M st ack CONSOLIDATED RESTful GUI CLI

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BUT WHY TAKE

THIS APPROACH?

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THE FUTURE OF BIG DATA

HITACHI – BIG DATA DRIVES BIG INNOVATION

Machine data is in our DNA

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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

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BIG DATA ANALYTICS

– VARIETY DOMINATES

RE LE V A NT T E CHNO LO G IE S RE LE V A NT T E CHNO LO G IE S

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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 database many more

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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.)

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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

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FOOD FOR

THOUGHT

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

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

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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

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

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

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QUESTIONS AND

DISCUSSION

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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

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