12nov13 big data analytics with teradata revolution analytics webinar 131112122130 phpapp02

8/13/2019 12nov13 Big Data Analytics With Teradata Revolution Analytics Webinar 131112122130 Phpapp02

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Revolution R Enterprise

Michele ChambersChief Strategy Officer & VP Product Managemen

@ Revolution Analytics

Bill FranksChief Analytics Officer @ Teradata


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Agenda Emerging Big Data Analytic Patterns

Teradata Overview


Teradata + Revolution R Enterprise

Q&A


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Polling Question #1: Rate your current use/ knowledge of R:

Actively Using R Now

Are planning on using < 6 mo.

Evaluation R now

Not at all


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Michele Chambers

Chief Strategy Officer & VPProduct Management

Revolution Analytics

@MCanalytics


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Todays Challenge: Accelerating Business Cad

Rapid Customer Facing Decisions

Score More Frequently

Need to Make Best Decision in Real Time

Changing Business Environment

Fact Based Decisions Require More Data

Need to Understand Tradeoffs and Best Course of Action Predictive Models Need to Continually Deliver Lift

Reduced Shelf Life for Predictive Models

Faster Time to Value

Reduce Analytic Cycle Time

Build & Deploy Models Faster

Eliminate Time Consuming Data Movements


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2nd Generation Pred


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Typical Challenges Our Customers Face

Big

DataMany new data

sources

Data variety &

velocity

Fine grain

control

Data

movement,

memory limits

Enterprise

ReadinessHeterogeneous

landscape

Write once,

deploy anywhere

Skill shortage

Production

support

Complex

ComputationExperimentation

Many small

modelsEnsemble

models

Simulation


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Big Data Big Analytics is diffe


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Polling Questions #2 What Analytics Challenges Are You Facing

Data Too Large for Current Solutions

Lack Timely Access To Needed Data

Slow Model Development or Execution

Inconsistent Solutions Across Platforms

Acceptability of Open Source

Cost of Solutions Too High None of the Above.


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Analytic Reference Architecture

Dec

ision

Integration

Anal

ytics

Data

Analytic Applications

Middleware

Analytic Tools

HadoopData

Warehouse

Other Data

Sources


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Architectural Approaches for Analytics

Dec

ision

Integration

Ana

lytics


Middleware

Analytic Tools

Data Hadoop Data Warehouse

Other Data

Sources

Beside Architecture

Local Data Mart

Dec

ision

Integration

Data&

An

alytics

Analytic Appl

Middlew

Analytic T

Hadoop Data Wareho

Inside Architecture


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Pros & Cons of Approaches

Beside

Architecture

Inside

Architecture

Hybrid

Architecture

Analytic workflow tasks performed in a separate analytics environme

database

Pros:Segregates analytic workload

Cons:Doesnt leverage powerful production for transformations, introduce

Analytics workflow tasks performed inside the source database with

Pros:Eliminates data movement, reduces model latency, allows exploratio

Cons:IT governance on production, potential new skills

Some analytic workflow tasks performed inside the source database

separate analytics environment

Pros:Leverages strengths of each architecture

Cons:Maintain multiple environments


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Building & Deploying Analytic Models

Beside

Architecture

Inside

Architecture

Hybrid

Architecture

Analytics A

LoData

Data Sources

234 1

Data&

Analytics Analytic Tools

Hadoop, DW, Other2 41

Legend3 Model recode / PMML2 Model build 4 M1 Data prep / marshaling

5

Analytics A

LoData

Data Sources

Analytic Tools

1 1 234

Th T d t U ifi d D t A hit t


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The Teradata Unified Data ArchitectureAny User, Any Data, Any Analysis

Data

Mining

Business

Intelligence

Applications

Marketing

DISCOVERY PLATFORM

INTEGRATED DATA WAREHOUSE

ERP

SCM

CRM

Images

Audio

and Video

Machine

Logs

Text

Web and

Social

SOURCES

DATAPLATFORM

TERADATA UNIFIED DATA ARCHITECTURE

ACCESSMOVEMANAGE

ANALYTICTOOLS

In-database &

managed servers
http://www.r-project.org/index.html


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Bill Franks

Chief Analytics Officer

Teradata

@billfranksga


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Teradata Highlights

Source: Gartner Magic Quadrant

Database Management Systems

2,400+ Customers in 77 Countries

10,000+ Employees

A Top 10 Public U.S. Software Company

Ranked 2nd Best Company in AmericaMotley Fool

Worlds Most Ethical Companies Ethisphere Institute

Leader in Data Analytics

Leader in Integrated Marketing Management


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Teradata Portfolio

Teradata is the global leader in

analytic data platforms, business

applications, and consulting

services that help organizations

become more competitive by

increasing the value of their data

and customer relationships.

Data Warehousing

Workload-specific Plat

Discovery Platforms

Integrated Marketing M

Consulting/Support Se

Partnerships with key

technology providers aintegrators


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In-DBSRequest

Sample Data

D e bt < 10 % o f I nc o me D e bt = 0%

Good

Credit

Risks

Bad

Credit

Risks

Good

Credit

Risks

Yes

YesYes

NO

NONO

Income>$40K

Results

Desktop and Server Analytic Architecture

In-Database Analytic Architecture

Results

Why Is Teradata Different?Server Based vs . In-Database Architectu res

ExponentialPerformance Improvem


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Raising The Bar With Teradata In-DatabaseAnalytics

Partnership CentricCollaborated with partners to enable in-DBS proc

2007

2013 Expanding P

Premier of Revparallel R in-D

Aster Data AcquisitionPioneer of big data analytic

2011

1998 Teradata Warehouse MinerPioneer of in-DBS mining

Data LabsSimplified In-DBS self-service Analytic

2010


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Teradata Data LabSelf-Service Analyt ic Sandboxes With Gove

ExternalData

Teradata

Mana

Data Labs

SASdata

csv

data

Data Lab(s) inside system to easily join toproduction data

Load experimental, untested data fromexternal sources

Rapid prototyping, exploratory andexperimentation analysis

An architecture design that enables

governance Works within your current data

warehouse environment

Teradata Data Lab portlets for IT andbusiness analyst


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The Teradata Unified Data ArchitectureAny User, Any Data, Any Analysis

Math

and Stats

Data

Mining

Business

Intelligence

Applications

Languages

Marketing

DISCOVERY PLATFORM

INTEGRATED DATA WAREHOUSE

ERP

SCM

CRM

Images

Audio

and Video

Machine

Logs

Text

Web and

Social

SOURCES

DATAPLATFORM

TERADATA UNIFIED DATA ARCHITECTURE

ANALYTICTOOLS

Data Lab


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Teradata Workload-Specific PlatformsFit Broad Range of Cus tom er Needs

Data MartAppliance

ExtremeData

Appliance

DataWarehouseAppliance

ActiveEnterprise

DataWarehouse

Appliance forHadoop

Aster BiAnalyticApplianc

Work-loads

Test /Development

or SmallerData Marts

AnalyticalArchive, DeepDive Analytics

StrategicIntelligence,

DecisionSupport

System, FastScan

Strategic /OperationalIntelligence,Real-Time

Update, Activeworkloads

Appliance for

Storing, Capturing

and Refining Data.

Hortonworks HDP

1.1

Discovery Pla

for Big Dat

Analytics w

embedded S

MapReduce

new data typ

and source

WE CAN COMPETE ON ANY WORKLOAD, AT ANY PRICE POINT, FOR ANY


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Revolution REnterprise

What is Revolution R

Enterprise?

How well does Revolution REnterprise play in the Big

Data zoo?


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High Performance, Scalable Analytics Portable Across Enterprise Platforms

Easier to Build & Deploy Analytics

is.the only big data big analytics platform

based on open source R

the defacto statistical computing language for

modern analytics

R i d d i l ti i ti


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Big Data In-memory bound Hybrid memory & diskscalability

Opevolu

Speed of

Analysis

Single threaded Parallel threading Shrin

Enterprise

Readiness

Community support Commercial support Deliv

prod

Analytic

Breadth &

Depth

5000+ innovative

analytic packages

Leverage open source

packages plus Big Data

ready packages

Supe

Commercial

Viability

Risk of deployment of

open source

Commercial license Elim

sour

R is open source and drives analytic innovationbut.has some limitations for Enterprises

I t d i R l ti R E t i (RRE)


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Introducing Revolution R Enterprise (RRE)The B ig Data Big Analyt ics Platform

DistributedR

DevelopR DeployR

ScaleR

ConnectR

Big Data Big Analytics Ready

Enterprise readiness

High performance analytics

Multi-platform architecture

Data source integration

Development tools Deployment tools

Th Pl tf St b St


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The Platform Step by Step:R Capab il i t ies

R+CRAN Open source R interpreter

UPDATEDR 3.0.2

Freely-available R algorithms

Algorithms callable by RevoR

Embeddable in R scripts

100% Compatible with existingR scripts, functions andpackages

RevoR Performanc

Based on o

Adds high-p

Available O PlatformTM

MicrosoftH

Windows&

Windows &

NEW Terad IBMNetez

IBM BigInsi

Cloudera H

Hortonwork

IntelHado


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The Platform Step by Step:


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DeployR Web servic

developmeanalytics via.NET APIs

Integrates Rinfrastructu

Capabilities

Invokes R Sweb service

RESTful inteasy integr

Works with leading BI &business ru

DevelopR Integrated development

environment for R

Visual step-into debugger

Available on:

Windows

The Platform Step by Step:Tools & Deployment

DevelopR DeployR


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DistributedR

ScaleR

ConnectR

DeployR

Write Once. Deploy Anywhere.

DESIGNED FOR SCALE, PORTABILITY & PERFOR

In the Cloud Amazon

Workstations &Servers Desktop

Server

Clustered SystemsIBM PlaMicroso

EDW NEW TeIBM Net

Hadoop Hortonw

Clouder



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Revolution R EnterprisePropels Enterpr ises into th e Future

De

cision

Integration

Ana

lytics

Data


Middleware

HadoopData

Warehouse

Other Data

Sources

One Big Data Big Analytics Platform


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One Big Data Big Analytics PlatformMult ip le Arch i tectures

De

cision

Integration

An

alytics


Middleware

Analytic Tools

Data Hadoop Data Warehouse

Other Data

Sources

Beside Architecture

Local Data Mart

De

cision

Integration

Data&A

nalytics

Analytic Appl

Middlew

Analytic T

Hadoop Data Wareho

Inside Architecture

RevoR & DistributedR & ScaleR

DeployR

DevelopR

DeployR

RevoR & Distrib

ScaleR & Develo

ConnectR

Revolution R Enterprise Powers


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Revolution R Enterprise PowersWrite Once, Deplo y An ywhere

Beside

Architecture

Inside

Architecture

Hybrid

Architecture

A

nalytics A

LoData

Data Sources

234 1

Data&

Analytics Analytic Tools

Hadoop, DW, Other2 41

Legend3 Model recode / PMML2 Model build 4 M1 Data prep / marshaling

5

Analytics A

LoData

Data Sources

Analytic Tools

1 1 234

Bottom Line: Save Time, Save Money, Get Insights Faster Direct connectors access data without data movement

Push down analyzing data without movement

Use same R script on any platform without recoding

Use right platform for the job!

The Power of Revolution R Enterprise


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The Power of Revolution R EnterprisePerform ance & Scalabi l i ty

Open Source Lev

V

a

l

u

e

RevoR 3-5

DistributedR Eff

DistributedR Po

DistributedR Ma

ScaleR Lab

ScaleR Lev

ScaleR Mo

ScaleR Mo


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Teradata Database

ODBC

Revolution R

Enterprise:

Database Nodes

Hybrid Storage

RRE in Teradata Database

Web Services

Desktop & Enterpr

Applications


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AMPs

Desktop & Enterprise

Applications

Desktops &

Servers

Teradata

Database

ODBC

Revolution R

Enterprise

Data

Segments

Database Nodes

Hybrid Storage

ParseEngine

External StoredProcedure

TableOperator

TableOperator

TableOperator

TableOperator

Web Services

How Does It Work? RRE in Teradata Database

Product Architecture & Feature Highlights


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Data Prep, Distillation & Descriptive Analytics

R Data StepDescriptive

Statistics

Statistical

Tests Sa

Data importDelimited, Fixed, SAS,

SPSS, OBDC

Variable creation & transformation

Recode variables

Factor variables

Missing value handling

Sort

Merge

Split

Aggregate by category (means, sums)

Min / Max

Mean

Median (approx.)

Quantiles (approx.)

Standard Deviation

Variance

Correlation

Covariance

Sum of Squares (cross product

matrix for set variables)

Pairwise Cross tabs

Risk Ratio & Odds Ratio

Cross-Tabulation of Data

(standard tables & long form)

Marginal Summaries of Cross

Tabulations

Chi Square Test

Kendall Rank Correlation

Fishers Exact Test

Students t-Test

oduc c ec u e & ea u e g g sRevolut ion R Enterpr ise ScaleR: High Performanc e Big Da

Product Architecture & Feature Highlights


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Statistical Modeling Mac

Predictive

Models Sum of Squares (cross product

matrix for set

variables)

Multiple Linear Regression

Generalized Linear Models (GLM) -All exponential family distributions:

binomial, Gaussian, inverse

Gaussian, Poisson, Tweedie.

Standard link functions including:

cauchit, identity, log, logit, probit.User defined distributions & link

functions.

Covariance & Correlation Matrices

Logistic Regression

Classification & Regression Trees

Predictions/scoring for models

Residuals for all models

Histogram

Line Plot

Scatter Plot

Lorenz Curve

ROC Curves (actual data and

predicted values)

NEWTree Visualization

Data

Visualization

Variable

Selection Stepwise Regression

(linear, NEWlogistic

and NEWGLM)

Monte Carlo

Cluster

Analysi K-Mean

Classifi

Decision

NEWDe

NEWPM

Simulation

Deploy

g gRevolut ion R Enterpr ise ScaleR: High Performanc e Big Da

R + Revolution R Enterprise


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R + Revolution R EnterpriseUnequaled B ig Data Big Analyt ics

Big Data

DistributedAnalytics

Performance

Enhanced RPerformance En

Big Data Distribut

Deploy Ana

Web, Mobile, Data Vi


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Teradata Database

ODBC

Revolution R

Enterprise:

Deploy R

Database Nodes

Hybrid Storage

Integrating Enterprise Apps

Web Services

Desktop & Enterpr

Applications


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Decision Layer: Examples on-demand analytics

On-demand sales

forecasting

Leveraging thepower of R from

Microsoft tools

Real-ti

se
http://deployr.revolutionanalytics.com:4000/


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RRE 7.0: Alteryx Integration Comparable to SAS Enterprise Miner, IBM SPSS Modeler (aka

Clementine)

Easy way to create analytic models with visual based analytic flo

R analytics

Readany data

source Create

analytic

flow


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RRE 7.0: Tableau Accelerator Makes analytics consumable to end users

Tableau integration is via .NET (creates a Tableau document wh

read by Tableau)


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RRE 7.0: MS Excel Accelerator Makes analytics consumable to most pervasive BI tool

Excel integration is .NET

R an

R visualization

Leveraging Teradata + Revolution R Enterprise


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g g pTeradata Database, DistributedR & ScaleR Scalable R-Based In-Database Transformation

High-Performance, Parallel R-Based Data Transformation

Augment Traditional ETL, ELT and SQL Methods Free R Users To Load and Transform Data Independently

Fast, In-database Exploration & Model Development

Vastly Accelerate Model Development, Validation and Re-Training

Eliminate Data Movement Penalties & Duplication Costs

Embrace In-database Machine Learning

Next Generation Big Data Big Analytics

Scalable To Meet All the Data All The Time Requirements

Integrate the Enterprise With In-Database Scoring

Make On-Demand Analytics a Reality For All Users


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Polling Questions #3 How would you consider applying Teradata + Revolution R Ente

Operations Risk Management

Customer Analytics


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How is RRE Used?

Discovering Patterns

with Big Data

Building Models

Efficiently

Flexibly

Models

Customer segmentation

Market basket analysis

Social networking

analysis

Fraud detection

Marketing attribution

Sentiment analysis and much more

Credit risk

Customer churn

Propensity to buy

Market risk

Operational risk

and much more

Custo

Pricin

Reco

engin

and m

Why Customers Buy


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Teradata & Revolution R Enterprise

Enabling Analytics Across the UDA Lower Cost & Risk of B

Supercharge R with TePowers R for the Enterprise

www.revolutionanalytics.com


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http://www.teradata.com/partners/Revolution-Analytics/


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www.revolutionanalytics.com/contact-us


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Thank you.

12nov13 big data analytics with teradata revolution analytics webinar 131112122130 phpapp02

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