1. This document is offered compliments of
BSP Media Group. www.bspmediagroup.com
All rights reserved.

2. HUAWEI TECHNOLOGIES CO., LTD.
FROM BIG DATA TO BIG VALUE
INFRASTRUCTURE NEEDS
AND HUAWEI BEST PRACTICE
HU YUHAI
MARKETING DIRECTOR, BIG DATA & CLOUD STORAGE
HUAWEI ENTERPRISE IT
NOVEMBER 2013
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3. DATA-DRIVEN INSIGHT
Making better, more informed decisions, faster
Raw Data
Capture
2
Store
Process
Insight

4. DATA LANDSCAPE CONTINUES TO EVOLVE
Data Volume
Captured and processed
Data Velocity
Of ingest and time
sensitivity for analysis
Satellite
Images
Data Variability Data format
Sensors
Email
BioInformatics
Documents
OLTP
Web Logs
BUSINESS PROCESS
Generated
STRUCTURED DATA
Social
Video
Audio
HUMAN Generated
UNSTRUCTURED DATA
1990
3
M2m Log
Files
2000
2008
2013
MACHINE Generated
SEMI-STRUCTURED DATA

5. BIG DATA ANALYTICS DATA FLOWS
Capture
Store
Process
Insight
OLTP
…
CRM
Terabytes
ERP
SCM
OLTP DB
SAN
ETL
MPP DW
Human
…
Machine
Web Logs
4
Petabytes
NAS
MPP Data Store
Converged Compute & Storage
Exabytes

6. EXAMPLE FOR “EXABYTE” REQUIREMENT
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"CERN is hitting the technology limits for resource-intensive simulations
and analysis. Our collaboration with Huawei shows an exciting new
approach, where their novel architecture extends the capabilities in
preparation for the Exascale data rates and volumes we expect in the
future." said Bob Jones, head of CERN OpenLAB

7. INFRASTRUCTURE REQUIREMENTS
EXISTING INFRASTRUCTURE DOESN’T SCALE !
 Scale capacity on demand
 Scale bandwidth on demand
 High throughput ingest
 Process data in place near real-time
 Cost effective, follows Moore’s Law
Scaling in every dimension is key !
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8. INFRASTRUCTURE NEEDS
 Scale-out distributed storage platforms
‒ Bring the computation to the data
‒ Can’t move Petabytes around network
‒ High throughput streaming workloads
‒ Batch oriented processing
 Colum-oriented NOSQL and MPP databases
‒ Flexible schemas, massive scale
 Real time analytics requires massive flows
‒ New platforms combine real-time with batch
‒ Trigger on events and process historical data
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9. HUAWEI STRATEGY ON BIG DATA
Intelligent Application Awareness
Multi protocol Interface
Openness and cooperation
Natively support Multi-workload
Integrated Storage, analysis and archiving functions
Huawei
Strategy
Data full life cycle management
Infrastructure is Key of Big Data
Scale out and X86 architecture, all IP based
Fully symmetric and distributed file system
“Build the Most Efficient Big Data Platform”
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10. HUAWEI ENTERPRISE-LEVEL BIG DATA PLATFORM
M&E
TELECOM
BANKING
WORKLOAD
High Performance
Store and Archive
STANDARD
EXPOSURE
NFS/CIFS/HDFS
Query and Retrieval
for Structured Data
SQL
GOVERMENT
Analysis Processing
for Unstructured Data
MR/HBASE
MPP DB
ENGINE
ENTERPRISE HADOOP
ENGINE
NATIVE
INTERFACE
HDFS
ENERGY
EB-level Storage
Resource Pool Mgmt
HTTP/S3
OBJECT STORAGE
ENGINE
• World Leading Performance and Scalability Storage
Platform as the Infrastructure.
OCEANSTOR
BIG DATA
FRAMEWORK
NATIVE
INTERFACE
• Natively Integrated HADOOP, MPP DB, OBJECT
Engine, Efficient Data Loading and Processing.
DISTRIBUTED
LOAD
QUOTA
STORAGE
RAID
BALANCE
MGMT
TIERING
• End-To-End Data Protection and Life CycleSYSTEM
Mgmt.
“HIGH SCALABILITY” DISTRIBUTED STORAGE
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11. OCEANSTOR 9000 BIG DATA STORAGE
No.1 Performance
5,000,000 OPS
No.1 Scalability
288 Nodes
5,500,000
5,000,000
4,500,000
4,000,000
3,500,000
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000
500,000
5,000,000
5,000,000 OPS
40 PB
3,064,602
1,512,784 1,564,404
1,112,705
EMC Isilon
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No.1 Capacity
NetApp
FAS6240
Avere OceanStor OceanStor
FXT3500
N8000
9000
3x
Performance

12. ENTERPRISE-LEVEL HADOOP PLATFORM
 Customized Hadoop
‒ Reliability improvements
‒ Redundancy, Failover, SPoF elimination
‒ Security/privacy improvements
‒ Encryption of data and metadata, KERBEROS access
control
‒ Management simplification
‒ GUI platform management tools, role-based admin
‒ All Hadoop tools, such as HIVE, PIG, etc.
 Innovative DR Solution
‒ DR site up to 1000km
 Special VM instances for Hadoop processing
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13. MANAGER SNAPSHOT
Dashboard – Overall System Status
Service Management
Resource Management
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14. “OCEANSTOR” BIG DATA PLATFORM HIGH LIGHTS
NFS/CIFS/HDFS
SQL
MR/HBASE
HTTP/S3
MPP DB
ENGINE
HADOOP
ENGINE
OBJECT
ENGINE
NATIVE
INTERFACE
NATIVE
HDFS
NATIVE
INTERFACE
• Multi-Workload Scale-Out
Storage Platform
• Leading Storage Efficiency and
Scalability
• End-To-End Data Protection
• Enterprise-Level Hadoop Model
DISTRIBUTED
RAID
LOAD
BALANCE
QUOTA
MGMT
STORAGE
TIERING
DISTRIBUTED STORAGE SYSTEM
• Native Integrated Hadoop/
MPP-DB/Object
• Unified Management
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15. HVS: NO.1 PERFORMANCE ENTERPRISE STORAGE
HVS85T / HVS88T
Critical
Business
Centralized
Virtualization
Storage
DR
• Smart Matrix Architecture
• Industry-leading RPO
• RAID2.0+ improves efficiency by
300%
1,000,000 IOPS 16 Controller
No.1 performance
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High scalability
7 PB
3 TB
No.1 capacity
No.1 cache

16. UDS: EB-LEVEL MASSIVE STORAGE SYSTEM
Universal Distributed Storage
Web
Disk
Space
Lease
Active
Archive
Centralized
Backup
• Native object storage,
decentralized architecture
ARM based high density hardware
SoD client
0 / 2 128
Pm
Hash
(key)
P0
P1
P2
DHT ring
P10
P3
P9
P4
P8
P7
• Unlimited Scalability: EB-level
capacity
P5
P6
• Extreme Reliability: 99.9999%
data durability
• Low TCO: Energy saving HW &
Zero-Touch design
DHT: Highly Available Key Value Store
2.1 PB
60%
45%
Capacity / rack
15
40 GB
Output BW / rack
Energy saving
Reduced TCO

17. HUAWEI IT BUSINESS COVERAGE
Applications
Servers
Big
Data
Storage
Converged
Infrastructure
Cloud
Computing
Data Center Facilities
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Management
Distributed Cloud Data Center

18. KEEP YOUR COMPETITIVE ADVANTAGE
 Big data is here
 Big data presents new challenges to infrastructure
 Be careful with an open source Hadoop
 Implementing a robust foundation and careful selection
of tools can allow you to benefit from big data
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19. HUAWEI TECHNOLOGIES CO., LTD.
THANKS!
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