The Efficient Big data Platform - IDC 360, Copenhagen
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Transcript of 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 TECHNOLOGIES CO., LTD. Huawei proprietary. No spread without permission. Page 3
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
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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
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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
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Analysis of X in 1 Big Data Technology
Huawei Enterprise Introduction
The Efficient Big Data Platform
1
2
Contents
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The Digital Universe
2020 = 35 ZB
2013 = 3.6 ZB
2009 = 0.8 ZB *Zettabyte = 1 trillion gigabytes
*Source: IDC Digital Universe Study
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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
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Capacity
Performance
Capacity vs. Performance
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Tools to tackle Big Data?
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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
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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
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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
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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
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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
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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.