© 2006 Cisco Systems, Inc. All rights reserved. Cisco ConfidentialPresentation_ID 1

Leveraging Big Data to Create Value

June 5th, 2014

3

Agenda

12-12:30pm Registration and Lunch

12:30-12:40pm Welcome and Introductions -- Art Hansen

12:40-1:45pm Keynote Presentation -- Chris Ward, Brian Vaughan, James Bigger

1:45-2:20pm Hadoop in the Real World by MapR -- David Feldman

2:20-2:30pm Break

2:30-2:45pm Cisco Unified Computing System Rack Mount Servers for Big Data – Wade Ison

2:45-3:30pm Big Data Brainstorm Breakouts

3:30-4:30pm Refreshments, Q&A Session, and Conclusion

4:30pm Raffle Drawing for iPad

Big Data as a Competitive Strategy

Harvard’s Michael Porter:

1. Cost Leadership Strategy (Wal-Mart)

2. Differentiation Strategy (Southwest)

3. Innovation Strategy (Apple)

4. Operational Effectiveness Strategy (UPS)

5. Technology-based Competitive Strategy

What do we have that makes us different?

• Custom Apps• Process (Workflow)• Big Data• People• Culture

5

Big Data’s Financial Benefits

Gartner predicts that “Big Data will deliver transformational benefits to enterprises within 2 to 5 years, and by 2015 will enable enterprises adopting this technology to outperform competitors by 20% in every available financial metric

Goals for Today:

• High ROI less than a year• Must be applied to things that

are important to the business• Use of multiple patterns

encouraged• New ways of correlating data

that was formally not correlated• Remember Big Data patterns

usually require scale

• Understand Big Data Major Building Blocks

• Learn the major patterns• Understand how to introduce Big

Data into the enterprise in practical ways

• Identify a solid use case for Big Data

Tips for Winning:

WWT Big Data Leadership Team

20 years of management consulting and entrepreneurial experience. Expertise in financial services, insurance and telecom. Prior consulting experience with Opera Solutions and A. T. Kearney. Ph.D. in Physics from Oxford University.

James BiggerPrincipal Consultant

15 years in management consulting, analytics and software experience. Expertise in healthcare and insurance. Prior experience with Opera Solutions, Mitchell Madison Group and Broadlane.Ph.D. in Physics from Stanford University.

Brian VaughanPrincipal Consultant

20 years in management consulting and executive leadership. Expertise in retail, marketing, hospitality & financial services. Prior consulting experience with Opera Solutions and The Boston Consulting Group.BA from Princeton University, MBA from the University of Virginia Darden School of Business.

Chris WardPrincipal Consultant

Over 20 years of experience in a range of IT and security disciplines. Responsible for deploying large, secure, Hadoop-based platforms for the U. S. Government. 10 year of international experience implementing networking and virtual data center environmentsUndergraduate degree from AIU.

Matt DuBellPrincipal Systems Engineer

Over 7 Years of experience in management and analytics consulting. Led engagements in telecom at Opera Solutions. Previous experience performing predictive analytics for NASA and USAF at The Aerospace Corporation.Ph.D. in Mechanical Engineering from Pennsylvania State University.

Yoni MalchiEngagement Manager

18 years of analytics and software development experience. Expertise in financial services, healthcare, insurance, retail and marketing science. Prior analytics development experience at Opera Solutions, FICO and J.D. Power and Associates.Ph.D. in Physics from Stanford University..

Jason LuChief Scientist

Over 7 Years of management consulting and entrepreneurial experience. Expertize in financial services, travel, and retail sectors across US and Europe. Led Big Data strategy and analytical engagements at Opera Solutions.MSci in Astrophysics from the University of Cambridge.

Jamie MilneEngagement Manager

Over 8 years of experience in analytics consulting and delivery management. Ran engagements in wealth management, corporate security, marketing, education and transportation at Opera Solutions and IBM Global Business Services.BS in Mathematics from Georgetown University.

Chris InfantiEngagement Manager

Over 20 years of experience in enterprise datacenter, building innovative solutions in Big Data, storage, HPC, virtualization, data migration and enterprise applications. Formerly lead architect for NetApp's Big Data solutions, and led the development of the FlexPod select solutions. B.S. in Electrical Engineering.

Prem JainPrincipal Architect

9

Volume, Variety and Velocity of Data are ExplodingThe production of data is expanding at an astonishing rate. Drivers include the switch from analog to digital technologies and the creation of structured and unstructured data by individuals and companies via social media and the Web

• Every 60 Seconds:­ 98,000+ tweets­ 695,000 status updates­ 11 million instant messages­ 698,445 Google searches­ 168 million+ emails sent­ 1,820TB of data created­ 217 new mobile web users

• The need to process more data faster to respond to dynamic business trends has brought new requirements for database architectures

• We believe the industry stands at the cusp of the most significant revolution in database and, therefore, application architectures in the past 20 years.

VelocityVarietyVolume

2010 2015 20200

10

20

30

40

ZB Enterprise Managed Data

Enterprise Created Data

2009 2010 2011 2012 2013 20140

10

20

30

40

50

60

70

80

Unstructured data storage

Structured data storage

EB

Source: IDC, Gartner, EMC, Worldwide File-Based Storage 2010-2014 Forecast

Vendor Landscape Is Crowded and Growing

Data Sources& Capture

IT Infrastructure

Data Management & Integration

Analytics Platforms and Solutions

Analytics Services and Support

Data Vendors Infrastructure VendorsOpen Data Platforms

Proprietary Data Platforms

Extended infrastructure + data platforms

Systems Integrators

Specialized End-to-End Solutions

Analytics Service Providers

Vertical Analytics Solutions

Distributed File System and Processing LanguageCharacteristics• Parallel

storage/processing• Flexible programming

model• Horizontal scaling• Batch processing

Non-relational Key-Value Database Characteristics• Fast read/write• Real time query• Horizontal scaling• Simple programming

model • Dynamic schema

Column-Oriented Analytics DatabaseCharacteristics• Relational• Efficient compression• Optimized for fast

read of many/all records

In-Memory Database and ProcessingCharacteristics• Relational• Random Access• Extremely Fast

Enablement / Uses• Complex Event

Processing• Real Time Analytics• Potential to use a

common database for transactions and analytics

Enablement / Uses• Pre-processing of data

for analytics• ETL for transforming

unstructured data to structured

• Data summarization

Enablement / Uses• Real-time ingest• Rapid retrieval• Input to MapReduce

Enablement / Uses• On-Line Analytics

Processing (OLAP)• Data storage and

retrieval for advanced analytics

Foundational Emerging

Key Big Data Technologies

11

Hadoop NoSQL Columnar In-Memory

12

The Big Data Software StackThe big data ecosystem includes open source and proprietary distributions that span the stack from ingest through analytics

Job

Flow

USER/MACHINE WORKFLOW

Enterprise Structured Enterprise Unstructured 3rd Party Web/ Unstructured

Flexible interfaces:

TRANSFORM

ANALYTICS DATABASE

ANALYTICS

ACCESS/QUERIES

INGEST

FILE SYSTEM/DATABASE

MANAGEMENT

ColumnarIn MemoryParallel RDBMS

EMC/PIVOTAL HD / GREENPLUMHP/VERTICA/

CLOUDERAORACLE BIG DATA

EXADATA/EXALYTICSIBM INFOSPHERE

BIGINSIGHTSSAP HANA

TERRACOTTA BIGMEMORY

ZOOKEEPER

CLOUDERA

HORTONWOR

KS

MAPR

PIVOTALHD

HADOOPCASSANDRA

HBASEMONGODB

TEREDATA

NETEZZA

GREENPLUM

VERTICA

OLAPNatural LanguageCustom Analytics

Custom API’sSQL

OPEN SOURCECOMMERCIAL

OPEN SOURCE

Fast, Scalable

Provisioning Maintenance

Flexible, Compressed, Fast Read

Optimized for high vol reads

Interfaces to accept data

Real Time & Batch

HDFSNoSQL - Document - Key-Value - Wide Column

SQLPIG

HIVE

RPYTHON

SAS

SPSS

BatchStreaming

SQOOPFLUME

SPLUNKTALEND

LAYER PROPERTIES OPTIONS EXAMPLES OF PRODUCTS INTEGRATED OFFERINGS

MapReduce HADOOP

Parallel, Distributed

ODSDataWarehouse

CallCenter

ServerLogs Financial Demographic

OO

ZIE

DATA

ACQUIRE

ORGANIZE

ANALYZE

DECIDE

SOLUTIONS

MICROSTRATEGY

BUSINESS OBJECTS

COGNOS

ORACLE OBIEE

PLUS

13

Technology: Expanding the Traditional StackBig Data requires a technology stack that leverages existing infrastructure and introduces new technology for distributed parallel processing

Queries (SQL)

Relational Databases

Monolithic Hardware(few CPUs and network

computers)

“Shared Disk/Memory” Architecture

(centralized processing)

Direct Record Access or Queries

Monolithic Hardware(few CPUs and network

computers)

“Shared Disk/Memory” Architecture

(centralized processing)

NoSQLDatabase

Parallel Relational Database

DistributedFile

System

High-Performance Traditional Relational Database

MapReduce Programs

Distributed Hardware(multicore CPUs, multiple computers

connected via high-performance network)

“Shared Nothing” Architecture(distributed parallel processing)

INTERFACE

DATABASE/ DISTRIBUTED PROCESSING FRAMEWORK

HARDWARE

TRADITIONAL RELATIONAL DATABASE STACK STACK FOR THE NEW DATA

FOUNDATION

Source: IDC, CSC, Gartner

14

Business Need

Class of Analytics

Analytics: Translating Business Needs to MathRegardless of industry, many use cases translate into a limited class of “math problems” that big-data platforms (unlike transactional platforms) are optimized to solve at scale

Method Analytics Ready Stack

Hardware & Software

• Parallel

• Distributed

• Shared Nothing

• Columnar

• NoSQL

• In-Memory

• ARMA

• Decision Trees

• Genetic Algorithms

• Graph Theory

• Kalman Filter

• KNN

• Linear Regression

• Logistic Regression

• Matrix Factorization

• Monte Carlo

• Neural Networks

• Sorting

• Survival Time Analysis

• Visualization

• Regression

• Classification

• Clustering

• Forecasting

• Optimization

• Simulation

• Sparse Data Inference

• Anomaly Detection

• Natural Language Processing

• Intelligent Data Design

• Recommendation

• Risk Scoring

• Pricing

• Capacity Planning

• Cost Reduction

• Matching

• Retrieval

15

Defining The Business Opportunity Is The Starting PointThe power of “Big Data” lies in bringing together data in a timely fashion from sources within and external to the enterprise - structured and unstructured - to create a complete view of critical business issues, therefore enabling advanced analytics to unlock key insights that drive significant business value

Outcome

Analytics

Data

Technology

Clearly defined use cases with the potential to deliver significant value by distilling vast data into new, previously unknowable intelligence

Advanced machine learning techniques to analyze data and mine for insights to drive critical business decisions

Structured or unstructured, internal or external, requiring new methods of storage/integration

Emerging/new technology stacks using scalable, distributed architectures

Telematics is Transforming Auto Insurance

Big Data Use CaseCombine driving behavioral with actuarial data to create individualized risk models that more accurately predict claims losses that enables risk adjusted pricing to gain market share and increase margins

Business ImperativeTo gain profitable market share, insurance companies need to offer the lowest “risk adjusted” pricing possible to consumers

Methods• KNN• Linear Regression• SVD

Class of Analytics• Regression• Clustering• Anomaly Detection

• Sensors to capture routes, miles driven, time of day, braking patterns, driving speed

• Geospatial maps tied to database layers

Science & Data

HDFS

MapReduce

NoSQL

Data W/H

In databaseAnalytics

Data Marts

Technology

Data

16

C a s e S t u d yI n s u r a n c e

Predictive Maintenance

17

FTP over MESH 

Data Logger

Data Logger

• One per truck• (Logs, Sensors, OEM

Alarms, VIMS Service Port)

Equipment Maintenance

Dispatch & Operator

Fuel, Oil Analysis, etc.

Hours

1 Urgent Component Problem

2 Critical Sensor Problem

Stratifying Alarms1

3 Important/Not Urgent Component/Sensor Problem

4 Not Important Component or Sensor Problem

5 Noise - Ignore

Data Logger

Data Driven Preventative Maintenance

Data/Analytics driven timing for preventative maintenance (e.g., oil changes) on individual Trucks

3

1 Urgent Component Problems

e.g., Engine, Transmission, Differentials, Torque Converters, Final Drives

Major Component Failure Model(s)2

Project Scope• 252 Trucks – 200

sensors per truck• 7 Mine sites• 10,000

readings/second

Data Integration• Integrating 15+ siloed data sources

in multiple file formats• 10 Terabytes of data• 3 year historical data ecosystem

Business Impact: Higher equipment up-time; reduced critical component failure; better preventative maintenance and increased labor productivity

C a s e S t u d yM i n i n g

Data Warehouse Augmentation: Value PropositionAugmenting the Data Warehouse with a less expensive Hadoop system will allow companies to free up valuable space on their DW systems to run faster queries and analysis, whilst storing large volumes of their data universe

WWT Hadoop Appliance Traditional Data Warehouse

Full Data UniverseCRM Social

MediaBilling

Web logsPayments

Scheduling

Cold Data Warm Data Hot Data

2. About 50% of data that is brought into a typical Data Warehouse system is rarely accessed: Cold Data

3. About 80% of the queries and reporting performed on Hot Data does not need to be at DW speeds

1. A significant amount of data is thrown out during the ETL process that may be valuable in the future

Traditional Data Warehouse

Full Data UniverseCRM Social

MediaBilling

Web logsPayments

Scheduling

Cold Data Warm Data

2. Store Cold Data in Hadoop, taking advantage of lower cost per TB

− Teradata: $17K− Hadoop: $2K

3. Continue to take advantage of DW agility and speed in real-time analysis and querying

1. Utilize additional Hadoop-based storage to store full data universe

− Files can be stored in natural format

Warm Data

Hot Data

Potential jumping-off point for Big Data Business Impact project

CURR

ENT

PRO

POSE

D

Integrating Many Data Sources To Provide Lift

Purchase History

ServiceHistory

Web Data

Campaign Metadata

Destination Word clouds

Partner Hotels

Profiled 100+m transactions for

millions of customers

Linked data for millions of customer

interactions and service records

Analyzed billions of page-views for

behavioral indicators

Extracted meaning from tens of

thousands of email campaigns

Mapped destinations to key “feature tags”

which explain selection

Geotagged tens of thousands of partner

hotels by understanding free

text description

C a s e S t u d yG l o b a l A i r l i n e

19

Time

Nov 2010

Dec 2010

Jan 2011

Feb2011

Mar2011

Apr2011

May2011

Jun2011

Jul2011

Aug2011

Sept2011

Hotel ExperienceFlight Car Rental HolidayCustomer Travel ProfileID= xxxx

0%10%20%30%40%50%60%70%80%90%

100%

% Offered

Upt

ake

% Lift

0%10%20%30%40%50%60%70%80%90%

100%

% Offered

Upt

ake

%

Time

Nov 2010

Dec 2010

Jan 2011

Feb2011

Mar2011

Apr2011

May2011

Jun2011

Jul2011

Aug2011

Sept2011

Hotel ExperienceFlight Car Rental HolidayCustomer Travel Profile: ID= xxxx

Typically social media tools focus on monitoring past/present activity. Predictive analytics allows users to identify important threads and intervene early, shifting the focus to future activity

• Details on particular themes or attributes

• Forecasts trend and a mechanism to intervene in attribute that are going viral

• Word cloud shows ongoing buzz and sentiment

• Tabular view shows emerging themes and sentiment, virality score and recommended time-window for action

Social Media AnalyticsC a s e S t u d yC o n s u m e r

E l e c t r o n i c s

20

Curriculum Management

Engine

Curriculum Management Engine

We designed a recommendation engine that generates a dynamic set of recommendations on a daily basis (over 1MM/day, from sales force handhelds, website, call centers) that learns and adapts to increase its ability to change behaviors over time through a Curriculum Management Engine

Plan for Smith Household:

Total Wallet = $600Aspiration: Achieve 60% share of wallet up from 40%How:• Habituate Pizza and Ice

Cream and Increase Frequency

• Move Into Dinner Entrees & Sides

• Move Into Higher Margin Breakfast Entrees

• Increase Frequency of Purchases

VISIT #1:1. Haven’t Bought In A While:

2. Others On My Route Like:

3. Would You Like Another?:

4. Just for You -- $1.00 Off

Household Response

VISIT #21. Would You Like Another?

2. Others On My Route Like:

3. No pizza; not yet consumed

4. Just For You

cNature of Recommendations

• Individuated Offers – Especially for You

• Cross-Sell/ Up-sell – Based on latent needs

• Reminders – Haven’t bought in a while

• Trials – Never tried but similar people like it

• Promotions – Being a loyal customer

Recommendations for Grocery Retailer’s Customers Delivered $100 million p.a. in EBIT

C a s e S t u d yF o o d G r o c e r

Using Internal and External Data with Advanced Analytics for Site Selection

• Comprehensive performance data– Fronts store / pharmacy sales– Customer and patient demographics– Local area demographic

• Web Scraping and Text Analytics– Neighborhood business profile– Competitor performance– Healthcare alternatives (ER, Urgent Care, PCPs)

Enriched Dataset

Advanced Analytics

• Non-linear, multivariate predictive models– Linear/Logistic Regression– Decision Trees (CART)– Random Forest– Gradient Boosting Machine – Neural Networks

• Incorporation of all data, including variables usually viewed as “qualitative”

Gravity Mapping

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

R = 0.75

M o d e l P e r f o r m a n c e

Predicted Patient Volume

Actual Patient Volume

+17%

Model Recommendation

0.83

Original Expansion

Plan

0.71

Potential Volume

I m p a c t

C a s e S t u d yR e t a i l P h a r m a c y

23

Designing Appropriate Reference ArchitecturesA reference architecture is a specific set of software and hardware components that together comprise an Analytics-Ready Infrastructure

USER/MACHINE WORKFLOW Visualization Forecasts Pricing Reports Alerts Scores Offers

NETWORK

LAYER DESCRIPTION EXAMPLES OF PRODUCTS

DATA

FILE SYSTEM/DATABASES

Enterprise Structured Enterprise Unstructured 3rd Party Web/ Unstructured

ODSDataWarehouse

CallCenter

ServerLogs Financial Demographic

CUSTOM ANALYTICS

ANALYTICS TOOLS

ANALYTICS DATABASES• Flexible, Compressed, Fast Read• Columnar, In Memory, Parallel

RDBMS

• High-level programming languages with packaged analytical modules

• Can be either general purpose or industry/function specific

• Services• Advanced models

• Parallel, Distributed• HDFS or NoSQL

• Interfaces to accept fast and varied data

“Analytics-Ready Infrastructure”

COMPUTE

STORAGE

INGEST

• 10Ge, low latency

• Commodity, rack mount• Purpose built servers• Internal JBOD, Direct Attached,

Network

SAS R PYTHON SPSS

VERTICA GREENPLUM TERADATA NETEZZA

EXADATA SAP HANA

CLOUDERA MAPR HORTONWORKS

PIVOTALHD

MARKLOGIC

DATATACTICS

ORACLE NOSQL

FLUME SQOOP TALEND VELOCIDATA

UCS-C240 UCS-C460 HP 380P HP SL4540

UCS 6200 NEXUS 2200 HP 5800 DELL FORCE10

JBOD SATA JBOD SSD E-SERIES ISILON

24

Deploying new technologies and combining with existing architecture

• How do we create an effective integrated Big Data stack?

• What new technologies do we need and how do they fit together?

Organizing for success • Where does Big Data fit? • What belongs in the BUs vs.

centralized?• Who is responsible for data

integrity?• Where do we find the critical

resources needed to deliver Big Data solutions?

Navigating a crowded and evolving vendor landscape

• How do we separate marketing hype from reality?

• Who should we use? Who can we trust

Defining the business value proposition

• What problem/opportunity are we pursuing?

• What is the value that can be created?

Four Major Big Data Challenges Facing Most CompaniesIn our meetings with customers, four issues are consistently brought up as a major challenges related to creating a big data capability that can effectively support the business units

Key Big Data

Challenges

Dual Approach to Delivering Big Data SolutionsWWT offers customers both strategic and tactical approaches to derive value from the application of Big Data analytics and technology

25

• Strategic Roadmap− Big Data Strategy− Use Case Design

• Use Case PoC− Analytics Development− Workflow Integration

• Data Warehouse Augmentation− ETL Offload− Data Lake Creation

• SAP HANA Implementation • Big Data Stack Build / Optimization• Production Support & Sustainment

BIG DATA BUSINESSIMPACT

Extract value from data to drive multiple Use Cases

BIG DATA TECHNOLOGY OPTIMIZATION

Accomplish data tasks, faster, cheaper, better

EXAMPLE SCALE OUT HARDWARE• Multiple Nexus 6000/

7000 Series switches• 5 – 50 Big Data racks• Cisco SAP HANA scale-out

(e.g. 8-16 UCS-B200)• Software scale-out

EXAMPLE STARTER KIT:Cisco SAP HANA Medium Appliance (2 UCS-C460)• Big Data Solution Stack:

o 2 UCS 6296PPo Each Big Data rack:

2 Nexus 2232PP 8-16 HP DL380 or SL4540, UCS-C240, etc.

o Initially: 1 – 2 rackso Software: MapR, E.

Service and Solution Offerings

26

• Develop a roadmap for implementing Big Data ­ Use case exploration­ Data Governance,

Infrastructure and Analytics ownership

• Define high impact use cases

• Design and test appropriate reference architectures

Plan Design Pilot Scale

WWTOfferings

IndicativeInfra-

structure

• Create detailed description of selected pilot use cases ­ Analytics­ Workflow

integration

• Test various reference architectures

• “Stand-up” reference architecture

• Design the pilot­ Success criteria­ Timeline­ Scope

• Identify and prepare data

• Build analytical models

• Design workflow

• Implement, manage and monitor

Analytics-Ready Infrastructure Solution Development

• Implement design changes from pilot learnings

• Invest in software development as necessary to improve UI

• Prepare ETL process for scale

• Build out infrastructure as required to support rollout

4. Production Support• Operationalizing POC• Infrastructure Sustainment• Training• Ongoing support

3. Proof of Concept• POC design• Analytical models• Customer data loaded,

processed and analyzed

1.Strategic Roadmap• Use case definition• Organizational alignment• Big Data Architecture high

level design

2. Big Data Stack Build• Detailed design Big Data

architecture and BOM• Procure, configure and

deploy Big Data stack

Advanced Technology Center (ATC)

COLLABORATIONENTERPRISE NETWORKS

SECURITY DATA CENTER

A highly collaborative, ecosystem to design, build, educate, demo & deploy advanced technology solutions for our customers & partners

Hands-on Access to over $50M in Equipment

• Point Product Demos• Tech. Training Sessions • EBCs / ATC Tours • Tech Days Demos• Customer Proof of Concepts• Reference Arch. Dev. • Product Training / PS• Version Upgrade Testing

• Version Upgrade Testing• Strategic Ref. Arch. Demo (RAD)• Product Comparison –Func. • Product Comparison – Perf.• Customer Access to Lab • Customer Environment• Workshop Demos• Early Field Trials / Beta Code • Certification

•Next GenerationNetworking•Nexus (7K, 5K, 3K & 2K)•Virtual Networking(Nexus 1000v)•OTV, LISP, Fabric Path•Layer 2 Extension•DR/BC Networking

• BYOD (Bring Your Own Device) & Secure Mobility

• Jukebox• ISE & RSA• ASA 1000v• VSG (Virtual Security

Gateway)• Cyber Security Solutions

• Unified Communications

• Tandberg Video• VXI (View &

XenDesktop)• WebEx, Call Center &

Collaboration Solutions• Phones, Backpacks &

Soft, Phone Clients• Telepresence & Business

Video

• Vblock, FlexPod & CloudSystem Matrix

• EMC & NetApp Storage• vSphere / XenServer• vCloud Director• VDI (View / XenDesktop)• Cisco CIAC & BMC CLM• EMC’s UIM & Cloupia • FAST MDC (Mobile Data

Center) Solutions

27

28

ATC Big Data Functions: OverviewThree functions of the ATC have been identified, which will support Sales (and other) processes

Function Description Usage

Proof of Concept

• Test customer solutions prior to full onsite implementation, e.g.

− Run Use Case analytical models and architectures on Big Data machines

− Create Big Data hardware/software stack, potentially with client data

• Mid-term project basis, to provide an environment for customer, based on a running engagement

Technology Comparison

• Compare Big Data solutions to provide insight into strengths and weaknesses of each

• Run “bake-offs” to gauge how well a full solution can be solved using certain components

• To test generic POCs, may be customer-driven

• Inform Big Data Team on best solutions

Field Demo • Showcase Big Data capabilities by hosting demos of WWT PoCs and analysis

− Run Use Case analytical models and architectures on Big Data machines

• Tool for sales calls and EBCs

Big Data Environment Set-up: ATC Reference Architectures

29

Four analytics-ready infrastructure stacks have been developed in the ATC to showcase Big Data technologies

DATAEnterprise Structured Enterprise Unstructured 3rd Party Web/ Unstructured

ODSDataWarehouse

CallCenter

ServerLogs Financial Demographic

STORAGE

REFERENCE ARCHITECTURE 1

NETWORK

FILE SYSTEM/DATABASES

ANALYTICS TOOLS

ANALYTICSDATABASES

COMPUTE

INGEST

REFERENCE ARCHITECTURE 2

HP Internal Local Storage

UCS – NetApp Direct Attached Storage

UCS 6296UP NEXUS 2232PP

UCS-C220M3

REFERENCE ARCHITECTURE 3

UCS – Isilon Network Storage

UCS 6296 NEXUS 2200

HAWQ HBASE

PIVOTALHD

UCS-C240

MICROSTRATEGYMICROSTRATEGY

REFERENCE ARCHITECTURE 4

SAP HANA

HITACHI

UCS B BLADES

JBOD SATA

HORTON

IMPALA

NEXUS 2200

HP DL 380

HBASE

R PYTHON R PYTHONR PYTHON

HITACHINETAPP E5460 ISILON

VELOCIDATA

VELOCIDATA

VELOCIDATA

MAPR

CLOUDERA CLOUDERAGEMFIRE

IMPALA HBASE

JAVA JAVA JAVA

In ProcessCurrent In Process

SPLUNK SPLUNK SPLUNK

HORTON MAPR HORTON MAPR

CLOUDERA

SAP HANA

VELOCIDATA SPLUNK

30

First Step: Big Data Workshop