The Efficient Big data Platform - IDC 360, Copenhagen

www.huawei.com

HUAWEI TECHNOLOGIES CO., LTD.

The Efficient Big Data Platform Petri Pekkarinen, Marketing Director, IT Product Line, Huawei Enterprise

IDC's 360 Degress Conference in Copenhagen January 23rd 2014

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Huawei Enterprise Introduction

The Efficient Big Data Platform

1

2

Contents


Huawei - A Global Company

The

Netherlands

Germany

Hungary

Romania

Mauritius

Brazil

Argentina

Mexico

UAE

Bahrain

India

Malaysia

China

14 regions and 140+

branches with business

operations

150,000 employees from

150+ countries

28 joint innovation centers

and 45 training centers

Technical assistance center

R&D center

Shenzhen-based HQ

Accounting Shared Services Center

Logistics center and transit station

Training center

Bid center (in construction)

A Global Footprint


ICT

convergence

era

Networking

Collaboration

Security

Virtualization

Big Data

CT

IT

2009 1950’s 2000’s 1990’s 1970’s 2013……

Client/Processing

terminal/Data terminal

Mainframe

computer/TC

Server/Client

Mobile narrowband

Analog

communications Digital

communications

Mobile

broadband

Driving ICT Convergence to New Dimension

IT

Consumerization

IT

Virtualization

IT

Mobilization


Huawei Enterprise ICT Product and Solution Portfolio

Data Center

Infrastructure

UC&C

Enterprise Network &

Enterprise Wireless

Security

Unified Management Bring Your

Own Device

Software Defined

Network

Cloud Data

Center

ICT

VC/TP Telepresence IVS UC

Server Storage VDI

DC Network OTN/MSTP/Microwave IP Network GSM-R/LTE

Networking Power

Contact Center


Analysis of X in 1 Big Data Technology

Huawei Enterprise Introduction

The Efficient Big Data Platform

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2

Contents


The Digital Universe

2020 = 35 ZB

2013 = 3.6 ZB

2009 = 0.8 ZB *Zettabyte = 1 trillion gigabytes

*Source: IDC Digital Universe Study


Conflicting 4 V Model

• Near real-time responses

• Low value density

• Variety of sources

• Diverse data types

• Large capacity

• Volumes are groving quickly

Volume Variety

Velocity Value

IDC: four characteristics of big data


Capacity

Performance

Capacity vs. Performance


Tools to tackle Big Data?


Driving Force of Big Data

• Storage — open architecture, gradual scale out, and integration of near-line systems and offline systems

• Archiving — lifecycle management of mass data at a low cost and high energy efficiency

Big Data

• Maintenance — easy to use

• Analysis — compound data, unified storage of multiple data sources, unified query, and sharing of storage resources

1

2

3

4

Collection

Storage

Management

Analysis


Paradigm shift in Big Data architecture design

Proprietary Hardware

Commodity Hardware

Central Storage

Distributed Storage

Scale-up Scale-out

Serial Computing

Parallel Computing

Pa

rad

igm

sh

ift

in b

ig d

ata

arc

hit

ec

ture

des

ign

Core

Big Data

Life Cycle

Platform


Huawei Big Data Storage Strategies

Advanced platform architecture

Industry's highest-performance

distributed big data storage system

Fundamental innovation philosophy

3 in 1 architecture

Maximized data value

Fine-grained data lifecycle

management on a single platform

Core

Be

st

eff

icie

nc

y p

latf

orm

of

big

data


Big Data Life Cycle Platform

Big Data

Life Cycle

Platform

Flexible Scale-Out Platform

Information silos eliminated

Multiple interfaces and ability to

integrate

Reliable and redundant

Efficient and Intelligent

Unified Management

Big Data Infrastructure must achieve high efficiency besides meeting the

requirements of capacity, performance and diversity of data


Mount Sinai

• Mount Sinai hospital in New York optimized its patients first 8-12 hours in hospital by running hundreds of simula-tions.

• Achieved financial effect of adding 100 new beds without actually adding new

Spotify

• The heart of Spotify is a massive and growing data-set.

• Most data is user-centric

• Data is utilized to provide music recommendations, advertisement and many other things.

Amazon Bank data repository

Big Data in Practice

• Customer data predicted to grow from 2 PB to 9 PB by 2015.

• Big Data solution applied to compliance requirement of keeping customer transaction records for 15 years.

• Amazon harvests the buying patterns (transactions) or its customers to recommend complementary products for up-sell.

• It also uses community-generated ratings and tips to further guide buying decisions

Source: Supply Demand Chain Executive 2013, Spotify Labs 2013


Figure out the

business case. What

are your business

goals?

Be Strategic.

Decide what questions need to be asked. What are the pain points?

Get tactical.

Where the data will come from? Identifying right data is the key.

Be analytical.

Get buy-in from the whole team and locate right people. Big Data is all about team work.

Get Buy-in.

MEASURE RESULTS

Getting Started with The Big Data Initiative

Define Objective

Activate teams

Implement Activity

Measure results

Thank you www.huawei.com

Copyright©2011 Huawei Technologies Co., Ltd. All Rights Reserved. The information in this document may contain predictive statements including, without limitation, statements regarding the future financial and operating results, future product portfolio, new technology, etc. There are a number of factors that could cause actual results and developments to differ materially from those expressed or implied in the predictive statements. Therefore, such information is provided for reference purpose only and constitutes neither an offer nor an acceptance. Huawei may change the information at any time without notice.