Many organizations are struggling to understand Big Data, what it is, and how to best harness it. Generated by mobile devices, social media, click streams, machines, applications, and more, data is exploding at an exponential rate from sources that are increasingly complex and varied. How do you manage and leverage both structured and unstructured data? How do you use advanced analytics to gain new insights, find anomalies, correlations, and answers that can transform the business? Learn how enterprises are implementing Hadoop to get the answers to these questions and more.

1. © 2014 MapR Technologies 1© 2014 MapR Technologies

2. © 2014 MapR Technologies 2
MapR Overview
BIG
DATA
BEST
PRODUCT
BUSINESS
IMPACT
Hadoop
Top Ranked
Production
Success

3. © 2014 MapR Technologies 3© 2014 MapR Technologies
3 Trends
Forcing a revolution in enterprise architecture

4. © 2014 MapR Technologies 4
Industry Leaders Compete and Win with Data1TREND
More Data Beats Better Algorithms
Collecting interaction data from ecommerce, social media, offline, and call centers
enables a “customer 360 view” and consumer intimacy
Competitive Advantage is Decided by 0.5%
Consumer financial services: 1% improvement in fraud means hundreds of millions of dollars
Advertising and retail: 0.5% improvement in lift means millions of dollars increase in profitability

5. © 2014 MapR Technologies 5
Big Data is Overwhelming Traditional Systems
• Mission-critical reliability
• Transaction guarantees
• Deep security
• Real-time performance
• Backup and recovery
• Interactive SQL
• Rich analytics
• Workload management
• Data governance
• Backup and recovery
Enterprise
Data
Architecture
2TREND
ENTERPRISE
USERS
OPERATIONAL
SYSTEMS
ANALYTICAL
SYSTEMS
PRODUCTION
REQUIREMENTS
PRODUCTION
REQUIREMENTS
OUTSIDE SOURCES

6. © 2014 MapR Technologies 6
Hadoop: The Disruptive Technology at the Core of Big Data3TREND
JOB TRENDS FROM INDEED.COM
Jan ‘06 Jan ‘12 Jan ‘14Jan ‘07 Jan ‘08 Jan ‘09 Jan ‘10 Jan ‘11 Jan ‘13

7. © 2014 MapR Technologies 7© 2014 MapR Technologies
And 3 Realities

8. © 2014 MapR Technologies 8
OPERATIONAL
SYSTEMS
ANALYTICAL
SYSTEMS
ENTERPRISE
USERS
1REALITY
• Data staging
• Archive
• Data transformation
• Data exploration
• Streaming,
interactions
Hadoop Relieves the Pressure from Enterprise Systems
2 Interoperability
1 Reliability and DR
4
Supports operations
and analytics
3 High performance
Keys for Production Success

9. © 2014 MapR Technologies 9
Hadoop is Being Used to Drive Small, Rapid Decisions2REALITY
High Arrival Rate Data
• Clickstream
• Social media
• Sensor data, …
Business Impact
• Revenue optimization
• Risk mitigation
• Operational efficiency

10. © 2014 MapR Technologies 10
Architecture Matters for Success3REALITY
FOUNDATION

11. © 2014 MapR Technologies 11
FOUNDATION
Architecture Matters for Success3REALITY
Data protection
& security
High performance
Multi-tenancy
Workload
management
Open standards
for integration
NEW APPLICATIONS SLAs TRUSTEDINFORMATION LOWERTCO

12. © 2014 MapR Technologies 12
World-Record Performance on Cisco UCS
PREVIOUS
RECORD: 1.6 TB
with 2200 nodes
1.65 TBIN 1 MINUTE
298 NODES
NEW MINUTESORT WORLD RECORD
MapR: With a Fraction of the Hardware
Previous Record
Get the most out of your
hardware infrastructure

13. © 2014 MapR Technologies 13© 2014 MapR Technologies
MapR: Hadoop Real World Examples

14. © 2014 MapR Technologies 14
Largest Biometric Database
in the World
PEOPLE
20 BILLION
BIOMETRICS
National identification
system in India for all
citizens
Fingerprint and retinal scan
images and citizen data
1 trillion+ ID verifications
per week, geographically
dispersed across 8 data
centers
About 600m “residents”
enrolled
Requires 100ms response
times; zero data loss and
cross-datacenter replication

15. © 2014 MapR Technologies 15
Helping Farmers: Software and Insurance
• Help farmers protect and improve their farming operations
• Use machine learning to predict weather & other agribusiness elements
• Combine hyper-local weather monitoring, agronomic data modeling, and
high-resolution weather simulations
• Project weather for 2.5 years at every 20x20 plot across the US
• Climatology simulations need to quickly experiment at small scale and
then scale reliably
• MapR Hadoop to analyze >10 trillion data points from 2.5million sensors
• Faster machine learning performance enables more/faster simulations
• MapR M7 enables geospatial database backed by Amazon S3
OBJECTIVES
CHALLENGES
SOLUTION
Lower risk with new insurance products through better data analytics
Business
Impact
“85% of farmer risk is weather-related. MapR has enabled us to provide a class of weather insurance
that was not available before, helping farmers protect their operations.”
IT Director, Climate Corporation

16. © 2014 MapR Technologies 16
Cisco was able to analyze service sales opportunities in 1/10 the time, at 1/10 the cost,
and generated $40 million in incremental service bookings in the first year.
Cisco: 360° Customer View
Cisco uses integrated customer data to increase revenues
• Create shared view of customer & operations across 75,000 employees
• Increase revenue opportunities with sales partners
• Customer information was siloed in different divisions
• Customer interactions were inconsistent and not satisfying
• Missed opportunities for upselling/cross selling
• Use MapR to collect customer information across touch points
• Integrate billing, support, manufacturing, social media, websites, dial-in data
• Generate new sales leads internally and for partners
OBJECTIVES
CHALLENGES
SOLUTION
Architecture for
Sales Partner Opportunities
Business
Impact

17. © 2014 MapR Technologies 17
Financial Services: Recommendation Engine & Real-time Targeting
Making personalized real-time offers to credit card customers
• Increase revenue and customer loyalty with real-time personalized offers
• Increases revenue and improves customer experience through real-time targeting
• A more flexible, scalable platform that’s a fraction of the cost of traditional technologies
• Ensures reliability with MapR’s high availability and disaster recovery features
• Many different CRM tools and siloed targeting engines
• Developers and analysts are unable to access all customer data
• Want to increase speed and relevance of recommendations
• MapR M7 centralizes analytics and operational apps on one platform
• Integrates all customer online and offline data into HBase in real-time:
card member spend graph, merchant data, location, and feedback
• Centralized customer data repository provides more accurate insights
• Uses Mahout machine learning to provide real-time personalized offers
OBJECTIVES
CHALLENGES
SOLUTION
Business
Impact
GLOBAL FINANCIAL
SERVICES
CORPORATION

18. © 2014 MapR Technologies 18
Rubicon Project: Ad Optimization
Rubicon Project runs a real-time automated advertising platform
• Create open ad platform for over 100K global advertising brands and over
500 of the world’s premium publishers
• To keep up with their rapid growth, they needed to move to a
fault-tolerant, high-availability Hadoop production system
• Hadoop had become central to their operations but they were having
problems with instability
• Their 330-node Hadoop cluster processes 1M records/second
• They chose MapR for enterprise features such as high availability, data
protection and recoverability, disaster recovery, redundancy, and support
OBJECTIVES
CHALLENGES
SOLUTION
“Our company cannot run without Hadoop and MapR. We rely on MapR’s self-healing
HA, disaster recovery and advanced monitoring features to conduct 90 billion real-time
auctions on our global transaction platform.” Jan Gelin, VP of Engineering, Rubicon Project
Business
Impact

19. © 2014 MapR Technologies 19
Operational Apps: Push Messaging Platform
MapR: Enabling the “smartest, most aware, precise, easy-to-use, scalable,
secure and powerful push messaging platform on the planet"
• Enable organizations to build one-on-one brand relationships
• Push messaging and geo-location targeting that
• Support large numbers of customers in a multi-tenant platform
• Target specific consumers in real time with relevant offers
• Increase reliability of push messaging while lowering data center costs
OBJECTIVES
CHALLENGES
SOLUTION
• Increasing engagement and customer loyalty for 100’s of leading brands
• Reduced hardware footprint by 50%
• Consolidated 8 Hadoop clusters into 1 MapR cluster
Business
Impact
• MapR Distribution for Hadoop with Apache HBase for operational workloads
• Data placement control enables efficient cluster resource management

20. © 2014 MapR Technologies 20© 2014 MapR Technologies
Enterprise Data Hub Case Studies

21. © 2014 MapR Technologies 21
Data Warehouse Optimization
Improve data services to customers while reducing enterprise architecture costs
• Provide cloud, security, managed services, data center, & comms
• Report on customer usage, profiles, billing, and sales metrics
• Improve service: Measure service quality and repair metrics
• Reduce customer churn – identify and address IP network hotspots
• Cost of ETL & DW storage for growing IP and clickstream data; >3 months
• Reliability & cost of Hadoop alternatives limited ETL & storage offload
• MapR Data Platform for data staging, ETL, and storage at 1/10th the cost
• MapR provided smallest datacenter footprint with best DR solution
• Enterprise-grade: NFS file management, consistent snapshots & mirroring
OBJECTIVES
CHALLENGES
SOLUTION
• Increased scale to handle network IP and clickstream data
• Reduced workload on DW to maintain reporting SLA’s to business
• Unlocked new insights into network usage and customer preferences
Business
Impact
FORTUNE 100
TELCO

22. © 2014 MapR Technologies 22
Mainframe Offload & Optimization
Free up MIPS with Hadoop to Lower Cost and Modernize Data Architecture
• Reduce costs: defer expensive mainframe upgrades and reduce MIPS
• Maintain business SLA’s
• Open standards: convert gradually to next-gen data architecture (Hadoop)
• Connect and transform unique data formats (EBCDIC vs. ASCII)
• Skills shortage: Hadoop and mainframe (COBOL & JCL)
• Reliability and flexibility of alternate systems
• Syncsort connectivity and data conversions on MapR
• MapR uniquely handle small files without additional ETL steps to meet SLA
• MapR only Hadoop distribution with reliability mainframe customers expect
OBJECTIVES
CHALLENGES
SOLUTION
 Reduce storage costs: Go from $100K/TB to $1K/TB by migrating data to Hadoop
 Use MIPS wisely: Save average of $7K per MIPS by offloading batch jobs to Hadoop
 Deliver powerful new insights: combine mainframe data with big data for deep insights
Business
Impact

23. © 2014 MapR Technologies 23© 2014 MapR Technologies
Security and Risk Mgmt. Case Studies

24. © 2014 MapR Technologies 24
Solutionary: Managed Security Services Provider
Threat detection on real-time streaming data via platform as a service (PaaS)
• To address their growing customer base by processing trillions of messages (petabyte)
per year while continuing to provide reliable security services
• To improve data analytics by leveraging newer, more granular unstructured data
sources
”MapR has taken Apache Hadoop to a new level of performance and manageability. It integrates into
our systems seamlessly to help us boost the speed and capacity of data analytics for our clients.”
- Dave Caplinger, Director of Architecture, Solutionary
• Expanding existing database solution to meet demand was cost prohibitive
• The existing technology could not process unstructured data at scale
• Replaced RDBMS with MapR M7 to scale while retaining reliability requirements
• Reduced time needed to investigate security events for relevance and impact
• Improved data analytics, enabling new services and security analytics
• 2x faster performance compared to competing solutions
OBJECTIVES
CHALLENGES
SOLUTION
Business
Impact
Leader in Magic Quadrant

25. © 2014 MapR Technologies 25
Zions Bank: From SIEM to Fraud Detection
Cost effective security analytics and fraud detection on one platform
• To operationalize big data fraud detection: Fraud Operations and Security Analytics
team at Zions maintains data stores, builds statistical models to detect fraud, and then
uses these models to data mine and evaluate suspicious activity
• (Global bank fraud costs $200B annually)
“We initially got into centralizing all of our data from an information security perspective. We then saw
that we could use this same environment to help with fraud detection”
Michael Fowkes - SVP Fraud Operations and Security Analytics
• Existing technology infrastructure could not scale
• Timeliness of reports degraded over the last several years
• Chose MapR and cut storage costs by 50%
• Gained huge performance advantage – Querying time reduced from 24 hours to 30
min on 1.2 PB of data
• Leverage MapR scale for increased model accuracy and deeper insights
OBJECTIVES
CHALLENGES
SOLUTION
Business
Impact

26. © 2014 MapR Technologies 26
Cisco: Global Security Intelligence Operations (MSSP)
Operational and analytical security applications on one platform
• To protect customer networks through early-warning intelligence & vulnerability analysis
• To better react to evolving security threats in real-time
• Collect additional telemetry data from customers' firewalls, intrusion prevention systems
• Different analytical teams derived security intelligence in silos and lacked synergy
• Inability to scale with existing infrastructure to a million events per second from nearly
100 different channels over tens of thousands of distributed sensors
OBJECTIVES
CHALLENGES
SOLUTION
Business
Impact
• All analytic teams leverage a common platform leading to operational efficiencies
• Capability to scale - aggregating and analyzing millions of data points in real time
• Update customer networks with new threat footprints within a 2 to 5 minute window
• MapR M7: Central hub for all of the security analytics teams
• Stream, interactive, graph and batch processing on MapR with the flexibility to
perform closed-loop analytics across these functions in real time
• Key Features: Scale, enterprise-grade, operational efficiency and high performance

27. © 2014 MapR Technologies 27
Cisco SIO Hadoop Stack
SENSOR DATA
FIREWALL
LOGS
INTRUSION
PROTECTION
SYSTEM LOGS
Globally Dispersed
Datacenters
SECURITY
APPLIANCE LOGS
SQL Queries
and
Reporting
Batch
Processing
Graph
Processing
New Threat Footprint
within 2-5 min
Closed-Loop
Operations
Benefits: Unified platform for Analytics
 Low Operational Costs
 Faster Response Times
 Better Algorithms
MapR M7 Distribution for Hadoop
1 million events/sec. Over 100 channels
Spark
Streamin
g
for known threats
& aggregation
Mahout,
MLLib
Shark, Impala
GraphX &
TitanDB

28. © 2014 MapR Technologies 28
MapR is the Hadoop Technology Leader
BIG DATA
HADOOP

29. © 2014 MapR Technologies 29
MapR Distribution for Hadoop
MapR Data Platform
(Random Read/Write)
Data HubEnterprise Grade Operational
MapR-FS
(POSIX)
MapR-DB
(High-Performance NoSQL)
Security
YARN
Pig
Cascading
Spark
Batch
Spark
Streaming
Storm*
Streaming
HBase
Solr
NoSQL &
Search
Juju
Provisioning
&
Coordination
Savannah*
Mahout
MLLib
ML, Graph
GraphX
MapReduc
e v1 & v2
APACHE HADOOP AND OSS ECOSYSTEM
EXECUTION ENGINES DATA GOVERNANCE AND OPERATIONS
Workflow
& Data
GovernanceTez*
Accumulo*
Hive
Impala
Shark
Drill*
SQL
Sentry* Oozie ZooKeeperSqoop
Knox* WhirrFalcon*Flume
Data
Integration
& Access
HttpFS
Hue
NFS HDFS API HBase API JSON API

30. © 2014 MapR Technologies 30
MapR Summary
BIG
DATA
BEST
PRODUCT
BUSINESS
IMPACT
Hadoop
Top Ranked
Production
Success

31. © 2014 MapR Technologies 31
Q&A
@mapr maprtech
nitin@mapr.com
Engage with us!
MapR
maprtech
mapr-technologies