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Big Data Analytics
Eddie Toh
Regional Server Product Marketing Manager - Intel
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)
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, real time access
•  30x performance increase
$600 B
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
“In God we trust, all others bring data” — NASA, Johnson Space Center
How	
  Will	
  Businesses	
  Manage	
  a	
  50x	
  Data	
  Growth	
  	
  
by	
  2020	
  in	
  an	
  Affordable	
  Way?	
  
MACHINE	
  GENERATED	
  	
  
HUMAN	
  
GENERATED	
  	
  
BUSINESS	
  
GENERATED	
  
Sources of Big Data
EDGE	
  
SCALE	
  UP	
  
DISTRIBUTED	
  
REQUIRES	
  DIFFERENT	
  APPROACHES	
  
Hadoop?
The	
  best	
  thing	
  since…	
  
Hadoop Framework
Open	
  Source	
   Proprietary	
  
HDFS | Lustre | GlusterFS
Hadoop Compatible File Systems
YARN (+MapReduce)
Distributed Processing Framework
HBase
Zookeeper
Coordination
Flume
LogCollector
Sqoop
DataTransfer
Hive
Query
Oozie
Workflow
Mahout
Machine Learning
Pig
Scripting
R
Stats
Hcatalog
Metadata
Deployment	
  
Upgrade	
  
ConfiguraCon	
  
Unified	
  Logging	
  
Tuning	
  
Alerts	
  
Resource	
  
Monitor	
  
Job	
  Profiler	
  
Security	
  Controls	
  
Heat	
  Map	
  
Rhino (Security)
High Availability and Disaster Recovery
HBase	
  Explorer	
  
RecommendaCon	
  Engine	
   Behavior	
  Model	
   VerCcal	
  Accelerators	
  
AnalyCcs	
  Workbench	
  
Connectors
Netezza, Oracle,
SAP, SQLServer,
Teradata, DB2
Kafka
Event	
  Bus	
  
Lucene, Solr
Search	
  
Tribeca
Graph	
  Mining	
  
Gryphon
Low-­‐latency	
  SQL-­‐92	
  
Spark/Shark
In-­‐memory	
  
SLURM
Scheduler
Big Data Use Cases Across Industries
EducaCon	
  
Financial	
  Services	
  
Telco- China Mobile Group Guangdong
Hadoop & Xeon optimized Big Data storage & analytics
•  Challenge: Deliver real time access to Call Data
Records (CDR) for billing self service
•  Solution: Chose Hadoop + Xeon over RDMS to
remove data access bottlenecks, increase storage,
and scale system
•  Benefits: Lower TCO, 30x performance increase,
stable operation, analytics on subscriber usage for
targeted promotions
•  Data Characteristics:
•  30TB billing data/month
•  Real-time retrieval of 30 days CDRs
•  300k records/second, 800k insert speed/sec
•  15 analytics queries
Analy&cs	
  
Government - Smart Traffic Intelligent Transport System
Hadoop for Predictive Analytics
Crime prevention, Info sharing & Predictive Traffic
Analytics
Machine Generated Data:
•  Embedded HBase client in camera for real-time inserts of
structured/unstructured data
•  30000 + camera data collection points
•  2 billion HBase records
•  Petabytes of traffic data
•  Terabytes of images
•  1 week of Data mining
Results:
•  Automated queries for traffic violation
•  Crime Prevention: ID fake
•  Licenses <1 minute
•  Traffic Routing
App	
  	
  
Servers	
  
Regional	
  Data	
  Collec&on	
  
Distributed	
  Processing	
  Across	
  District	
  Nodes	
  
Derived	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Analy&cs	
  Services	
  	
  
Crime	
  PrevenCon	
   CiCzen	
  Traffic	
  Services	
  
Options For Hadoop Deployment
On-Premise (or private
cloud)
•  Limited scalability
•  Internal IT resources
to manage cluster
•  CapEx – HW, DC
space, power &
cooling
On AWS (public cloud)
•  Scalability
•  Flexibility
•  Easy to deploy to
multiple locations
•  Additional resources
on demand
•  OpEx
Hybrid Cloud model
•  Provides bursting capacity
•  Flexibility
•  Scalability
•  IT still needs to manage on-
premise cluster
Security Is Addressed In All Models
“Where do I start…?”
1.  What is your business problem?
2.  Do you have a (lots of) data problem?
3.  Will big data analytics work for my
business problem?
Speak To AWS Today!

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AWS Enterprise Day | Big Data Analytics

  • 1. Big Data Analytics Eddie Toh Regional Server Product Marketing Manager - Intel
  • 2. 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) 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, real time access •  30x performance increase $600 B 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 “In God we trust, all others bring data” — NASA, Johnson Space Center How  Will  Businesses  Manage  a  50x  Data  Growth     by  2020  in  an  Affordable  Way?  
  • 3. MACHINE  GENERATED     HUMAN   GENERATED     BUSINESS   GENERATED   Sources of Big Data EDGE   SCALE  UP   DISTRIBUTED   REQUIRES  DIFFERENT  APPROACHES  
  • 5. Hadoop Framework Open  Source   Proprietary   HDFS | Lustre | GlusterFS Hadoop Compatible File Systems YARN (+MapReduce) Distributed Processing Framework HBase Zookeeper Coordination Flume LogCollector Sqoop DataTransfer Hive Query Oozie Workflow Mahout Machine Learning Pig Scripting R Stats Hcatalog Metadata Deployment   Upgrade   ConfiguraCon   Unified  Logging   Tuning   Alerts   Resource   Monitor   Job  Profiler   Security  Controls   Heat  Map   Rhino (Security) High Availability and Disaster Recovery HBase  Explorer   RecommendaCon  Engine   Behavior  Model   VerCcal  Accelerators   AnalyCcs  Workbench   Connectors Netezza, Oracle, SAP, SQLServer, Teradata, DB2 Kafka Event  Bus   Lucene, Solr Search   Tribeca Graph  Mining   Gryphon Low-­‐latency  SQL-­‐92   Spark/Shark In-­‐memory   SLURM Scheduler
  • 6. Big Data Use Cases Across Industries EducaCon   Financial  Services  
  • 7. Telco- China Mobile Group Guangdong Hadoop & Xeon optimized Big Data storage & analytics •  Challenge: Deliver real time access to Call Data Records (CDR) for billing self service •  Solution: Chose Hadoop + Xeon over RDMS to remove data access bottlenecks, increase storage, and scale system •  Benefits: Lower TCO, 30x performance increase, stable operation, analytics on subscriber usage for targeted promotions •  Data Characteristics: •  30TB billing data/month •  Real-time retrieval of 30 days CDRs •  300k records/second, 800k insert speed/sec •  15 analytics queries Analy&cs  
  • 8. Government - Smart Traffic Intelligent Transport System Hadoop for Predictive Analytics Crime prevention, Info sharing & Predictive Traffic Analytics Machine Generated Data: •  Embedded HBase client in camera for real-time inserts of structured/unstructured data •  30000 + camera data collection points •  2 billion HBase records •  Petabytes of traffic data •  Terabytes of images •  1 week of Data mining Results: •  Automated queries for traffic violation •  Crime Prevention: ID fake •  Licenses <1 minute •  Traffic Routing App     Servers   Regional  Data  Collec&on   Distributed  Processing  Across  District  Nodes   Derived                                                                              Analy&cs  Services     Crime  PrevenCon   CiCzen  Traffic  Services  
  • 9. Options For Hadoop Deployment On-Premise (or private cloud) •  Limited scalability •  Internal IT resources to manage cluster •  CapEx – HW, DC space, power & cooling On AWS (public cloud) •  Scalability •  Flexibility •  Easy to deploy to multiple locations •  Additional resources on demand •  OpEx Hybrid Cloud model •  Provides bursting capacity •  Flexibility •  Scalability •  IT still needs to manage on- premise cluster Security Is Addressed In All Models
  • 10. “Where do I start…?” 1.  What is your business problem? 2.  Do you have a (lots of) data problem? 3.  Will big data analytics work for my business problem? Speak To AWS Today!