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APAC Big Data &

Cloud Summit 2013

Big Data Analytics & Hadoop

Use Cases

Eddie Toh

Server Marketing Manager

21 August 2013

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From the dawn of civilization until 2003, we humans

created 5 Exabyte of information. Now we create

that same amount of information in two days! In

2012, the digital universe of data will expand to 2.72

zettabytes (ZB). Then it’s predicted to double every

two years.

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Big Data – Volume, Velocity, Variety (& Value)

7.9 ZB by

2015 3x more bits in digital universe than stars in the physical universe

450 Billion Business transactions per day by 2020 (IDC)

$600 Bn Potential value to US healthcare

90% of Data In the world was created in the last 2 years.

100 years Worth of video uploaded to YouTube every 10 days

>5 Billion People calling, texting, tweeting & browsing on cell phones

How Will Businesses Manage a 50x Data Growth by 2020 in an Affordable Way?

“In God we trust, all others bring data” — NASA, Johnson Space Center

Therapies tailored to a persons genome

Decoding the human genome:

• From 10 years to hours

• On track to hit <$1000 per person

Explosive growth, 30 Tb/month billing data

Radical overhaul of customer service:

• Self service, realtime access

• 30x performance increase

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BIG DATA MACHINE

GENERATED

HUMAN GENERATED

BUSINESS GENERATED

Edge

Scale Up

Distributed

REQUIRES DIFFERENT APPROACHES

Govt.

Healthcare

Retail

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Big Data use cases across industries

Education

Financial Services

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Democratize data analysis from edge to cloud…

Intel can deliver end-to-end analytics from the edge

intelligent systems to the Datacenter/cloud

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End-uses & DR

Distribution System Transmission System

Energy Storage

Fuel Supply System

Fuel Source/Storage

Power Plants

Renewable Plants

Data Collection and Processing

Predictive Analytics

Sensors

Controllers

End-to-End Power Delivery Chain Operation

Monitoring, Ingestion, Modeling, Analysis, Coordination & Control

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Will Big Data be the difference between success and failure of a political campaign?

“This was the first

presidential election

campaign where all of the

data that was coming into

the campaign was

successfully collected and

centralized.”

“The Obama campaign

did a successful job with

that; the Romney

campaign did not”

John Aristotle Phillips, Chief

Executive of Aristotle International

(WSJ 11/29/12)

Eric Dishman Video

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From Intuition to Predictive Analytics Big Data maturity framework

Big Data

Adoption

Introduction

Deployment Production

Ongoing

Application

Business

Challenges

a) Cost

Reduction b) Competitive

differentiation

innovation c) Revenue Growth

• Revenue

Growth • Competitive

differentiation and

innovation

• Cost Reduction

• Obtain / maintain

customer Loyalty

targeted focus • Competitive

differentiation Innovation

• Revenue Growth

• Stabilizing revenue

generation

Intuition

vs.

Analytics Future strategies,

Day-to-Day

operations

Enhanced

Analytics

A data driven

enterprise

Descriptive Analytics • Financial and

Operation

Management

• Sales and Marketing

• Customer services

Prescriptive analytics • Strategic Business

development

• Research and product

• Development

• Enhanced Customer

services

Predictive analytics • Risk Management

• Real time customer

experience

• Automated resource

allocation

• Value creation and Brand

Management

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Bus. Strategy

KPIs

LOB

Reporting

Visual

Structures

Machine Learning

Algorithm

Analysis

Integration

Query Performance

Transport

Transformation

Warehousing

Efficiency

Trust

Workload

Governance

Tools

Network

Compute

Storage

Big Data Functional Models “Where do I start?”

Big Data

Infrastructure

Data

Management

Data

Usage

Hadoop

Framework

Data

Science Domain

Expertise

Value

Data Ingestion

and

Processing

Transform

question to

algorithm

Asking the

right

question

Driving Value from Big Data depends on Quality,

Accuracy and Efficiency

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Lastly… Intel® Distribution for Apache Hadoop*

Performance

Security Management

Intel

Architecture

Backup

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Intel* Distribution for Apache Hadoop What did we launch…

Focus on near real-time analytics w/ HBase & Hive enhancements

Access control, encryption, secure data movement

Job throughput efficiency for HDFS

Dynamic replication for HDFS & HBase

Intel optimized total solution architecture -distro, storage, network, compute

0

2000

4000

700

3500

Open

Sourc

e

Optimized Intel

IA/Distro

5X Performance

for Real-time

jobs

HBase as the data store. Query all CDR in month

− Inserting 10000 records/second/server

− Read from disk: >400 query/second/server

Intel® Manager for Apache Hadoop software Deployment, Configuration, Monitoring, Alerts, and Security

HDFS

Hadoop Distributed File System

YARN (MRv2) Distributed Processing Framework

HBase

Colu

mnar

Sto

re

Zo

ok

ee

pe

r Coord

inati

on

Flu

me

Log C

ollecto

r

Sq

oo

p

Data

Exchange

Pig Scripting

Hive SQL

Query

Oozie Workflow

Mahout Machine

Learning

R

connector

s Statistics

Intel enhancements

contributed back to open

source Open source components

included without change

Intel unique