Steve Jenkins from MapR Technologies presentation from our Big Data breakfast conference

1. Steve Jenkins
MapR Technologies
‘Business Opportunities for Big Data in the Enterprise ‘

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Big Data in the Enterprise
Steve Jenkins VP EMEA MapR Technologies

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Business Value

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Changing landscape
 90% digital data created in last two years
 2.7 Zettabytes in 2012 predicting 7.9 Zettabytes in 2015
• 1,000 Exabytes or 1 Billion Terabytes
 6 billion phone subscriptions
• 87% world population
 1.011 billion facebook users
• 604 million users login from mobile devices, monthly
 400 million tweets a day
• 84 million access by mobile

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Too much data ?
Retail industry
 39% infrequent collection, not fast enough
 42% could not link data at individual level
 45% not using effectively
 Only sample 10% of data

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 “The use of big data will become a key basis of competition
and growth for individual firms. In most industries,
established competitors and new entrants alike will leverage
data-driven strategies to innovate, compete, and capture
value from deep and up-to-real-time information.”
– McKinsey & Company
 “The size, complexity of formats and speed of delivery exceeds
the capabilities of traditional data management
technologies” – Gartner
 "The bringing together of a vast amount of data from public
and private sources, combined with the intuition of business
and thought leaders and the speed and affordability of
today's computers, is what Big Data is all about.”
– IDC

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Across all verticals, typical use cases
 Logistics
 Fraud
 Loyalty programmes
 Sentiment analysis
 ETA calculations
 Customer insight
 Gene sequening
 Operations

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Biggest challenges
 80% Finding talent
 72% Training and education
 76% Identifying the correct tools
 32% Siloed data, non cooperation
 Identifying the correct resources and use case
• Data Scientist
• Analytical capability
Based on 300 interviews by Infochimp

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From Big Data to Insights

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General Observations
 Analytics becoming a critical component in business environments
 Base decisions on data
 Work with existing applications
 Principle: keep all data around – benefit from all data
– Human generated
– Machine generated
 Pioneered at Google and Amazon

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Hadoop Growth

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The Hadoop Ecosystem

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Case studies – unlocking the power of Big
Data….
 Financial Services (customer insights, fraud detection, etc.)
 Global Telecommunications - Data Warehouse Augmentation
 Petroleum - Trade, Logistics & Transportation
 Retail application

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Case study – Credit Card Company
Fraud
detection
Personalized
offers
Fraud
investigation
tool
Fraud
investigator
Fraud
model
Recommendation
table
Queries on IT
logs
MapR Big Data Platform
Credit card
transactions

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Arrival of Big Data Impacts Data Warehouse
BIG
DATA
Volume
Variety
Velocity
Prohibitively expensive
storage costs
Inability to process
unstructured formats
Faster arrival and
processing needs
How can a Data Warehouse leverage Big Data?

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Case study – Data Warehouse Augmentation
 Problem:
– Major telecom vendor
– Key step in billing pipeline handled by data warehouse (EDW)
– EDW at maximum capacity
– Multiple rounds of software optimization already done
 Revenue limiting (= career limiting) bottleneck
 Solution: Use MapR to off-load ETL processes that don’t fit EDW
capabilities

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Clean Conform Normalize Present AccessTransformExtract
Billing
Systems
Clean Conform NormalizeTransformExtractExtract Clean Conform Transform Normalize
Present Access
Billing
Systems
Current ETL Pipeline
Hybrid Solution Pipeline
Teradata
Hadoop Teradata
DataStage
Data Warehouse Augmentation

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Results of TCO Evaluation
 CapEx: Cost avoidance for annual Teradata adds
 Storage: 20x storage good for next 5 years
 Cost: 100x cost reduction
 Scale Out Architecture: New nodes can be added on the fly
 No Disruption: Hybrid solution ensures no change to
upstream/downstream business systems
Solution Technology 5-Yr TCO
Existing Teradata $66,950,000
New Hybrid: Teradata + Hadoop $33,000,000
Total Cost Savings $33,950,000
One Time Hadoop Investment of ~$6.5M Provides $33.9M Cost Savings

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Use case – Geo-spatial & time series
dashboarding
 Data sources
– stock transactions
– vessel positions
– weather
 Goal: provide aggregated overview + drill-down capability in a
dashboard
 Batch-generated overview (Hadoop’s MapReduce, HBase/M7)
 Interactive, ad-hoc drill-down (HBase/M7, Apache Drill)

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Use case – Geo-spatial & time series
dashboarding
batch-generated overview
interactive, ad-hoc drill-down
storage and access at scale
dashboard

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Combine Different Data Sources
Streaming
writes to
Hadoop
Retail purchase Info
Real-time
offers
Hadoop
POS/Online
Data

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MapR

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MapR Distribution for Apache Hadoop
 Complete Hadoop distribution
 Comprehensive management
suite
 Industry-standard interfaces
 Combines open source
packages with
Enterprise-grade
dependability
 Higher performance

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MapR: The Enterprise Grade Distribution

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MapR Supports Broad Set of Customers
 Log analysis
 HBase
 Customer targeting
 Social media analysis
 Customer Revenue
Analytics
 ETL Offload
 Advertising exchange
analysis and optimization
 Clickstream Analysis
 Quality profiling/field
failure analysis
 Enterprise Grade
Platform
 COOP features
 Monitoring and measuring
online behavior
 Fraud Detection
 Channel analytics
 Recommendation Engine
 Fraud detection and Prevention
 Customer Behavior Analysis
 Brand Monitoring
 Customer targeting
 Viewer Behavioral analytics
 Recommendation Engine
 Family tree connections
 Global threat
analytics
 Virus analysis
 Patient care
monitoring
Leading RetailerGlobal Credit Card Issuer
 Intrusion detection & prevention
 Forensic analysis

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Thank You

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Industry Leaders Choose MapR in the Cloud
Google chose MapR to
provide Hadoop on Google
Compute Engine
Amazon EMR is the largest
Hadoop provider in revenue
and # of clusters