Teradata Aster: Big Data Discovery Made Easy

Big Data Discovery Made Easy (well, easier)

Brad Elo – Vice President Teradata Aster

Confidential and proprietary. Copyright © 2012 Teradata Corporation. 2

Teradata – The Clear Data Warehousing Leader Leadership in Execution and Vision

Gartner Magic Quadrant for Data Warehouse DBMS, 2013

The Magic Quadrant is copyrighted 23 December 2008 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the "Leaders" quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Ability to Execute

Completeness of Vision


Need for a Unified Data Architecture for New Insights

Enabling Any User for Any Data Type from Data Capture to Analysis

Discover and Explore Reporting and Execution

in the Enterprise

Capture, Store and Refine

Audio/ Video

Images Docs Text Web & Social

Machine Logs

CRM SCM ERP

Java, C/C++, Python, R, SAS, SQL, Excel, BI, Visualization


Big Data: From Transactions to Interactions

BIG DATA

WEB Petabytes

CRM Terabytes

Gigabytes

ERP

Exabytes

Increasing Data Variety and Complexity

User Generated Content

Mobile Web

SMS/MMS

Sentiment

External Demographics

HD Video

Speech to Text

Product/ Service Logs

Social Network

Business Data Feeds

User Click Stream

Web Logs

Offer History A/B Testing

Dynamic Pricing

Affiliate Networks

Search Marketing

Behavioral Targeting

Dynamic Funnels Payment Record Support Contacts

Customer Touches Purchase Detail

Purchase Record

Offer Details

Segmentation

Behavioral Analytics


Shift from a Single Platform to an Ecosystem

“Big Data requirements are solved by a range of platforms including analytical databases, discovery platforms, and NoSQL solutions beyond Hadoop.”

“We will abandon the old models based on the desire to implement for high-value analytic applications.”

“Logical” Data Warehouse

Source: “Big Data Comes of Age”. EMA and 9sight Consulting. Nov 2012.

1% 2%

2%

6%

8%

28%

26%

27%

Hybrid Data Ecosystem Nodes in Use

One Node

Two Nodes

Three Nodes

Four Nodes

Five Nodes

Six Nodes

Seven Nodes

Eight Nodes

Column-Store DBMS

CEP Content & XML

Complex Data Types

Cloud DBMS

Data in HDFS

In-Memory DBMS


AUDIO & VIDEO IMAGES TEXT WEB & SOCIAL MACHINE LOGS CRM SCM ERP

INTEGRATED

DATA

WAREHOUSE

UNIFIED DATA ARCHITECTURE

LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS

Engineers

Data Scientists

Business Analysts

Front-Line Workers Customers / Partners Quants

Operational Systems Marketing


• Single View of Your Business

• Cross-Functional Analysis

• Shared Source for Analytics

• Balance the Books

• Customer/Product Profitability

• Operational Applications

• CRM Execution

• Fast Time-to-Market For New Apps

• Mission Critical Service Levels

Integrated Data Warehouse Requirements



DISCOVERY

PLATFORM

INTEGRATED

DATA

WAREHOUSE



Engineers

Data Scientists

Business Analysts




Requirements of a Discovery Platform

DATA SOURCES

Structured Data

Multi-Structured

Data

Non- Relational

Data

DISCOVERY DISCOVERY TOOLS USERS

Discovery Platform Data

Scientist

Business Analyst

SQL

MapReduce

Statistical Functions

OLTP DBMS’s

• Structured and multi-structured data

• Doesn’t require extensive data modeling

• Doesn’t balance the books

• Data completeness can be good enough

• No stringent SLAs

• Fraud patterns

• Customer behavior

• Digital marketing optimization

• Supply chain and supply line sensors



DISCOVERY

PLATFORM

INTEGRATED

DATA

WAREHOUSE



Engineers

Data Scientists

Business Analysts



CAPTURE | STORE | REFINE


• Land/source operational data

• History or long term storage

• Preprocess data

• Transformations

Big Data Management Requirements

Machine Generated

Social Media



DISCOVERY

PLATFORM

INTEGRATED

DATA

WAREHOUSE



Engineers

Data Scientists

Business Analysts




Big Data

Analytics

Big Data

Management

Confidential and proprietary. Copyright © 2012 Teradata Corporation. 14 AUDIO & VIDEO IMAGES TEXT WEB & SOCIAL MACHINE LOGS CRM SCM ERP

DISCOVERY

PLATFORM


INTEGRATED

DATA WAREHOUSE

LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS VIEWPOINT SUPPORT

Engineers

Data Scientists

Business Analysts


Operational Systems Executives

TERADATA UNIFIED DATA ARCHITECTURE

Aster Connector for

Hadoop Teradata Connector

for Hadoop

Aster Teradata

Connector

SQL-H

Aster Loader Teradata Loader

SQL-H


Teradata Unified Data Architecture™ Partners Support Many Layers of the UDA

Updated: April 24, 2013


Summary

World is Moving Towards Hybrid Architectures - FAST

Teradata Provides the Most Complete Unfiied Data Architecture – includes Data Warehousing, Discovery, Hadoop & Spotfire

Discovery is Key Big Data Use Case – Optimally Done with Teradata Aster, Spotfire & Hadoop

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Teradata Aster: Big Data Discovery Made Easy

Business

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