3 Things to Learn: -How data is driving digital transformation to help businesses innovate rapidly -How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business -How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements

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Webinar:
How Data Drives Business
at Choice Hotels
Cloudera & Choice Hotels

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Our Speakers Today…
Vijay Raja
Sr. Solutions Marketing Manager
Cloudera
Robert Bushman
Principal Software Engineer
Choice Hotels

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Data is Transforming Businesses
DRIVE CUSTOMER
INSIGHTS
IMPROVE PRODUCT &
SERVICES EFFICIENCY LOWER BUSINESS RISK
MODERNIZE ARCHITECTURE

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Polling Question - 1
Which use cases are your organization using or planning to
use big data for?
A. Customer insights
B. IoT
C. Cybersecurity/Fraud
D. Data warehouse optimization
E. Other
(Multiple Choice Polling Question)

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Polling Question - 2
On what environment are you running your big data
platform (select one)?
A. On-premises
B. Public cloud
C. Hybrid (public & on-prem)
D. Not yet deployed but exploring options
(Single Choice)

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FILESYSTEM RELATIONAL
Cloudera Enterprise Data Platform
CLOUDERA ENTERPRISE

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Driving Insights from Big Data - The Value Chain
Handle data ingest
from diverse sources
Fundamentally
Secure
Data Ingest
Machine Learning
Capabilities
Diverse Analytical
OptionsCombine data from various sources
Enterprise Data Hub
Scale easily & Cost
effectively
Batch or Real- time
Data Streams
A comprehensive data management platform to drive business insights from data
Data Sources
Data Storage &
Processing
Serving, Analytics &
Machine Learning
Data Ingest
Diverse Data Sources
(Batch & Streaming)
Security, Scalability & Easy Management

9. Big Data Success Stories:
Choice Hotels and Cloudera

10. About Choice Hotels
6400 Hotels in 40 Countries Around The World

11. Use Cases

12. Traveler 360
• Problem
− Traveler data spans multiple channels and is most relevant in near-real-time –
faster than our traditional data warehouse can make it available.
• Solution
− Integrate data from multiple channels into a single ingestion pipeline.
− Use Lambda Architecture style speed and batch storage.
• Value
− Complete view of all the traveler’s information, regardless of system of origin.
− Near-real-time personalization of web, mobile, and point-of-sale interfaces.

13. Real-Time Franchisee Reporting
• Problem
− Tracking performance of our franchise properties can take days, leaving us in the
dark about rapidly evolving trends.
• Solution
− Ingest broad swaths of guest booking data into the data lake.
− Generate derivative datasets tailored to key business intelligence cases.
− Connect Tableau and other BI tools to the Data & Analytics Platform.
• Value
− Business analytics reports are available as soon as the data arrives.
− Evolving trends can be acted upon while they are fresh and hot.

14. Retiring Aging Systems
• Problem
− Aging systems – some more than a decade old – are being maintained for a few
remaining edge cases that do not fit in any new system.
• Solution
− Ingest legacy data into the Choice Data & Analytics Platform.
− Migrate old data pipelines onto DAP infrastructure.
− Reproduce existing views using modern tools like Spark and Impala.
• Value
− Cluster processing and high performance storage vastly improves performance.
− Legacy languages, libraries, and systems can finally be retired.

15. Swiss Army Knife
• PCI Compliance
• SOX Audits
• Historical Views
• BI Discovery
• Geo-Trends
• Glue

16. System Overview

17. Platform Architecture
IMPALA

18. Platform Architecture – Data Ingestion Layer
• Batch Ingestor
• RDBMS
• NoSQL
• Files (Logs, CSV)
• Stream Ingestor
• ActiveMQ / SQS
• Streaming of:
• File Sets
• RDBMS (CDC)
• Instead of Batch

19. Platform Architecture – Data Processing Layer
• Storage layer carved into logical buckets
• Landing, Raw, Delivery, and Derived
• Schema Stored With Data
• Platform Jobs for
• Converting Text Batches to Parquet
• Streaming Data to Parquet
• Compaction
• Derived Tables & Views
• Standardization

20. Platform Architecture – Data Delivery Layer
• Data Delivery
• Impala SQL (Tableau, SQL IDE)
• SparkR, RStudio, Sparklyr
• Spark to Web API (JSON, XML)
• Spark to Export (PDF, Excel, CSV)
• Self Service Derived Views
• Metadata driven
• Spark Refresh
• Near-Real-Time or Periodic
• Access Via SQL in Impala
• Access Via DataFrames in Spark
IMPALA

21. Deploying with Cloudera
• AWS & On-Prem
• Ability to Test-drive Hadoop Components Easily
• Configuration at Our Fingertips
• Effortless Upgrades
• Strong Road-Map for The Future

22. Key Factors for Success
• Separate Compute From Storage
− Enables “Bring Your Own Compute”
− Makes It Easier to Migrate Components
− Spin Up a New Cluster While Keeping the Old One Live
• Start Small and Build on Success
• Remain Agile, Embrace Change
• Get Business Users Involved Early
• Develop The Team: Your People are The Most Important Tool

23. Lessons Learned

24. Bootstrapping Big Data
• Pilot Proof of Concept
− Demo the Technology
• Scale Up to One Business Case
− Traveler 360
• Build Out Additional Cases
− Business Intelligence, Audit
• Stay Flexible, Explore and Discover
− Thrift -> Impala -> SparkSQL
− Sqoop, StreamSets, Custom JDBC, File, & MQ

25. The People
• Big Data is Hot: Everyone Wants to Do It
− Get Great People, Develop Ownership and Pride
• Big Data is Fun: Encourage Your Team to Enjoy It!
− Morale is Critical on Rapidly Evolving Projects
• Big Data Can Be Learned
− Passion for Learning is a Must; Experience is a Nice-To-Have
• Ramp-Up New Engineers Quickly
− Cloud, Virtual Machines, and Cloudera Make Ramp-Up Fast!

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Why customers are using Cloudera in the cloud?
Size compute and storage
independently, grow and shrink
clusters dynamically, and pay only
for what you use on ad-hoc,
transient workloads
Preserve business flexibility and
data portability and minimize
cloud lock-in by running in any
one of the three major public
cloud providers or in private cloud
Reduce risk with comprehensive
manageability, availability,
security, and governance required
for production big data workloads
Elastic Multi-Cloud Enterprise Grade

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Customers Across All Industries
Other
Financial
Services
Telecom
Healthcare &
Life Sciences
Public Sector
& Education

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Getting Started is Easy
1. 2.
Download or Deploy
in the Cloud
Sign up for Training Contact us or a Partner
to Start a POC
3.

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Thank you