The document discusses how Luminar, an analytics company focused on the Latino market, implemented a big data solution using Apache Hadoop. They partnered with Impetus Technologies for implementation strategy and architecture design, and used Hortonworks for the Hadoop distribution. The solution ingested diverse data sources to provide insights into Hispanic consumer behavior and help clients accelerate growth.

1. Break Through the Traditional Advertisement
Services with Big Data and Apache Hadoop
Jason Kirkland Franklin Rios Oscar Padilla
Sr. Dir. Systems Integrators - President – Luminar VP of Strategy – Luminar
Hortonworks an Entravision Company an Entravision Company
Anand Venugopal
Paul Codding
Director – Big Data Services
Solution Architect - Hortonworks
Impetus Technologies
© Hortonworks Inc. 2012

2. Quick House Keeping Rules
• Q&A panel is available for questions during the webinar
• There will be time for Q&A at the end
• We will record the webinar for future viewing
• All attendees will receive a copy of the slides and recording
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3. Agenda
Why Data Driven Business –
Hortonworks
A Big Data Success story – Luminar,
an Entravision Company
Jason Kirkland
Implementation Strategy – Impetus Sr. Dir. Systems Integrators -
Hortonworks
Technologies
Architecture Challenges and Design
– Hortonworks
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4. Why Data Driven Business?
Data driven decisions are
better decisions – its as simple
as that. Using big data enables
mangers to decide on the
basis of evidence rather than
intuition. For that reason it has
the potential to revolutionize
management
Harvard Business Review
October 2012
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5. Big Data: Organizational Game Changer
Transactions + Interactions
Petabytes
BIG DATA Mobile Web + Observations
Sentiment SMS/MMS
User Click Stream
= BIG DATA
Speech to Text
Social Interactions & Feeds
Terabytes WEB Web logs
Spatial & GPS Coordinates
A/B testing
Sensors / RFID / Devices
Behavioral Targeting
Gigabytes CRM Business Data Feeds
Dynamic Pricing
Segmentation External Demographics
Search Marketing
Customer Touches User Generated Content
ERP
Megabytes Affiliate Networks
Purchase detail Support Contacts HD Video, Audio, Images
Dynamic Funnels
Purchase record
Offer details Offer history Product/Service Logs
Payment record
Increasing Data Variety and Complexity
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6. Hortonworks Snapshot
We develop, distribute and support
the ONLY 100% open source
Enterprise Hadoop distribution
Develop Distribute Support
• We employ the core • We distribute the only 100% • We are uniquely positioned
architects, builders and Open Source Enterprise to deliver the highest quality
operators of Apache Hadoop Hadoop Distribution: of Hadoop support
Hortonworks Data
• We drive innovation within Platform • We enable the ecosystem to
Apache Software work better with Hadoop
Foundation projects • We engineer, test & certify
HDP for enterprise usage
Endorsed by Strategic Partners
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7. Big Data Success story
Franklin Rios Oscar Padilla
President – Luminar VP of Strategy – Luminar
An Entravision Company an Entravision Company
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8. About Luminar
● Luminar is an analytics and
modeling company focused on
helping clients achieve growth
Denver
and gain efficiencies
CO
● Build the first cloud-based
Santa Monica Washington
CA
Dallas
TX DC Big Data/ Hadoop analytics
environment in the U.S.
serving the Latino market
Mexico City
Mexico ● In-culture data scientist based
Data Scientist Resource
in Mexico and Argentina
● Key client segments include:
Retail, CPG, Financial Services,
Media & Entertainment,
Buenos Aires
automotive, and Publishing
Argentina
Data Scientist Resource ● Luminar is a business unit of
Entravision the leading media
group (NYSE: EVC)
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9. Latinos - the Fastest Growing US Segment
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10. How Hispanics are Marketed Today
Why is this a challenge?
● Investment decisions based
on limited information
● Sample or survey methods
breaks down at local level
● Limited insight value
● Campaigns often based on
52M people Filtered by sample, survey ethnically-coded data
diary and ethnic coding ● Little or no cultural relevancy
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11. This Approach Doesn’t Adequately Address
Fundamental Business Questions
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12. Factors that influenced Launching Luminar
1) We wanted to shift from the current marketing
paradigm targeting Latinos focused on sample data
2) We recognized that Hispanic consumers are under
represented with most marketing approaches
3) Our service offerings are synergistic to our parent
company
4) Our model would necessitate ingesting vast amounts of
diverse data that required a robust analytics
environment
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13. We Recognized that Insights is an
Evolving Process
• Testing market • Retains Hispanic • Increased ability to • Use analytics to
• No in-language agency capture, aggregate prescribe actions
experience • Use focus group and analyze data • Effective at sharing
• Mass media, single • Multi-channel, often • Use analytics to information and
channel not integrated guide actions insights
• No dedicated • Top Hispanic DMAs • Growing use of • Strong ability to
insights to guide capture, aggregate,
• Hispanic team • Bilingual experience analyze or share
future strategy
• Lack of • Qualitative data use information
understanding how • Strong use of insights
to use analytics to guide day-to-day
operations
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14. Latino consumer
“We deliver actionable insights that accelerate
Growth
growth and drive business efficiencies in
a complex consumer environment.”
Insights
Consumer environment
Efficiencies
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15. Big Data Brings a High-Value Offering
● Ability to more precisely support customers across the entire
marketing value chain:
– Move from a media & communications discussion to a business
challenge discussion
– Help identify growth opportunity within the Hispanic market
– Improve measurement of Hispanic market investments
– Demonstrate ROI
– Help accelerate growth through empirical data insights
● Transformative in the way we approached business and
marketing needs
● Leverage big data environment and 3rd party data sources
across business units
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16. A Vision and a Well Thought Out Plan
● It’s was a significant investment and commitment that required
CEO vision and support
● Developed detailed roadmap for success:
– Prepared comprehensive plan detailing operations, resources,
level of investment and implementation path
– We weighted the need for big data as new revenue source for
EVC
– We identified “packaged solutions” for a big data offering
– And, we clearly defined how big data fulfilled an underserved
market and provided a shift from sample-based research to
empirical analytics
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17. Approach:
Turning Data into Informed Actions
1
2
3
4
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18. A Simple but Powerful Approach to
Meet Customer’s Needs
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19. Luminar Big Data Would Need to
Support These Needs…
● Analytics-as-a-Service platform
● Aggregate multiple sources of data from diverse sources
– Licensed data
– EVC data
– Unstructured social data
– Client data
● Offer an advanced and unique focused analytics service
– Provide insights into Hispanic consumer behavior
– Targeting customers in retail, financial services, insurance and
auto segments
● Future offerings
– Platform as a Service
– White Label Services
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20. Importance of Aligning Our Vision with
the Right Technology Partner
● Proven track record – vendor had to have a demonstrable
experience in the implementation of big data solutions
● Technology agnostic –technology partner that could help
plan and deploy a solution architecture that was not married to
any one vendor
● Experience with multiple technology providers/suppliers
– We needed a partner that could understand the big data
landscape now, in 6 months and 18 months from today
● Blended team– Partner had to understand that they would be
operating in a blended client/vendor team environment
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21. A Successful Launch Would Require…
● Building a best-of-breed model based on Luminar’s needs:
– Lowest Total Cost of Ownership
– Have vendor neutral approach
– SQL data migration
– Cloud based architecture
● Other requirements included:
– Maximize “re-use” of vendor experience in Big Data
– Scalability to meet ongoing data licensing needs
– Meet data security requirements
– Deliver data visualization to meet client deliverables
– Start with a “shoestring” budget, grow as needed
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22. Build the right foundation for growth
• Impetus lead solution architecture and vendor selection
process
• We established a solution framework that delivers four client
offerings
• We architected a solution that defined all major technology Key
Performance Indicators (KPIs) and SPOF
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23. Implementation Strategy
– Building “Best of Breed” Foundation
Anand Venugopal
Director – Big Data Services
Impetus Technologies
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24. Who is Impetus Technologies
Advisors Experience. Architects Expertise. Applications Excellence.
• Advisors - Accelerated Consulting & Services Leader for Big Data.
• Architects - Pioneers in distributed software engineering.
Dedicated Innovation Labs
• Applications - Vertical Domain Expertise - Open source and
Innovation Product Portfolio.
We Implement Big Data
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25. Advisors Experience. Architects Expertise. Applications Excellence.
We Implement Big Data
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26. Domains - Big Data Use-Case Discovery
Verticals
Big Data
Analytics Value
Use-Case Drivers and
Patterns Functional areas
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27. Big Data roll-out Strategy
Business PoV/ RoI
Goals, Locate
Strategy Collate BIG
Budget
DATA Technology
Identify best
Design and
POC subset of
use-cases
Implement
Implement Operations
Production Integrate People
Optimize Add use-cases
We Implement Big Data
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28. Dashboards
We created a custom-scoring matrix used for evaluating
vendors pros and cons, defining requirements, and
weighting against Luminar’s objectives
We Implement Big Data
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29. Advisors Experience.
Architects Expertise.
Applications Excellence.
We Implement Big Data
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30. Architecture Challenges and Design
Paul Codding
Solution Architect - Hortonworks
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31. Challenges
• Project Schedule
• Data Loading
• Existing R scripts
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32. Cluster Provisioning
• Amazon EC2 Instances with EBS storage were used for
all components
• An Amazon Virtual Private Cloud (VPC) was created for
the cluster
• Entire cluster was provisioned in less than 4 hours
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33. Security Group: HDP_EXT Security Group: HDP_N AT
Tableau Server Tableau Desktop Gateway Server R-Studio Server N AT
ELIP & Private IP ELIP & Private ELIP & Private IP ELIP & Private IP ELIP
HDP Data N ode 1
Private IP
R-Hadoop Serve HDP JobTracker
Private IP Private IP
HDP Data N ode 2
Private IP
HDP Data N ode 3
Private IP
HDP Ambari
M ySQ L ServerIP
Private
HDP Data N ode 4
Private IP
HDP Data N ode 5
Private IP
HDP Data N ode 6
Hive, HCatalog, HDP Secondary Private IP
HDP N ameN ode
O ozie N ameN ode Private IP
Private IP Private IP
HDP Data N ode 7
Private IP
Security Group: HDP_IN T
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34. Architecture
Hive
M ySQ L Server Sqoop
JobTracker
Data N odes N ameN ode
Data N odes
Data N odes
Data N odes
Secondary
N ameN ode
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35. Technologies Used
• MapReduce/HDFS
• Sqoop
• Hive
• Hive ODBC Driver
• Talend Open Studio for Big Data
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© Hortonworks Inc. 2012

36. Jason Kirkland Franklin Rios Oscar Padilla
Sr. Dir. Systems Integrators - President – Luminar VP of Strategy – Luminar
Hortonworks an Entravision Company an Entravision Company
Anand Venugopal
Paul Codding
Director – Big Data Services
Solution Architect - Hortonworks
Impetus Technologies
© Hortonworks Inc. 2012

37. Next Steps
Questions
ChannelMarketing@Hortonworks.com
Bigdata@impetus.com
Download Hortonworks Sandbox
www.hortonworks.com/sandbox
Sign up for Training for in-depth learning
hortonworks.com/hadoop-training/
Follow…
@hortonworks, @hortonworks_U
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