2. Choice Hotels’ journey to better understand its customers through self-service analytics
Narasimhan Sampath & Avinash Ramineni
Strata Hadoop World | New York City | September 29th, 2016

3. Agenda Who is Choice Hotels Platform Architecture Implementation
Value Add
Strata Hadoop Word | New York City | September 29th, 2016

4. Who is Choice Hotels?
Strata Hadoop Word | New York City | September 29th, 2016

5. Who is Choice Hotels? Canada Hotels open 323
United States & Caribbean
Hotels open 5,276
Hotels under development 606
Rooms open & under dev. 446,813
Canada
Hotels open 323
Hotels under development 45
Rooms open & under dev. 30,135
South America
Hotels open 64
Hotels under development 7
Rooms open & under dev. 9,737
Asia Pacific
Hotels open 315
Hotels under development 25
Rooms open & under dev. 23,289
Europe
Hotels open 402
Hotels under development 31
Rooms open & under dev. 50,388
Mexico
Hotels open 28
Hotels under development 4
Rooms open & under dev. 3,219
Central America
Hotels open 14
Hotels under development 0
Rooms open & under dev. 1,468
Middle East
Hotels open 1
Hotels under development 2
Rooms open & under dev. 564

6. How About a Technology Company?
Strata Hadoop World | New York City | September 29th, 2016

7. Evolution of Guest Experience
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Strata Hadoop Word | New York City | September 29th, 2016

8. Project Goals Business Drivers Self Service Reporting and Analytics
Requirements for near real-time analytics
Simplify Governance, Compliance and Auditing
Better support for new applications
Technical Drivers
Unable to handle volume, velocity, and veracity
Retire Legacy Systems
Difficult to find skillset (Informix 4GL)
Simplify Technology Stack
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Strata Hadoop Word | New York City | September 29th, 2016

9. Key Design Tenets Separation of Compute and Storage
Independently scale compute and storage
Data Democratization and Governance
Bring your own Compute (BYOC)
Lift and Shift between cloud provider(s)
and On-premise
HA / DR
Open Source Stack

10. Separation of Compute and Storage
Scale storage and compute independently (up or down)
Shifts bottleneck from Disk IO to Network
Centralized Data Storage
Write once & read everywhere
Data Democratization
Easier Hardware upgrade paths
Flexibile Architecture
Servers
Storage

11. BYOC (Bring Your Own Cluster)
Eliminates the need for very large clusters
Easier to administer and maintain
Reduces multi-tenancy issues
Clusters can be upgraded independently
Enables on-demand computing
Lower costs
Marketing
Cluster
Centralized Storage
Personalization
Main

12. Platform Architecture

13. Platform Architecture – Data Ingestion Layer
DB Ingestor
Stream Ingestor
Kafka and Spark Streaming
File Ingestor
FTP / SFTP / Logs
Ingestion using Service API

14. Platform Architecture – Data Processing Layer
Storage layer carved into logical buckets
Landing, Raw, Derived and Delivery
Schema stored with data (no guesswork)
Platform Jobs for
Converting text to Parquet
Saving streaming data Parquet
Derivatives
Compaction
Standardization

15. Platform Architecture – Data Delivery Layer
SQL - Spark Thrift Server / Impala
Tableau, SQL IDE, Applications
SparkR
Self Service
Derivatives
Represented Via SQL on Delivery Layer
Stored in Derived Storage Layer
Metadata driven
Derived Layer Generators
Long running Spark Job
Derivative Refresh

16. Implementation CDH Cloud ready-ness Cloudera Director Limitations
Multi-Availability zone, regions
Spark Thrift Server
Support
Performance Tuning
Concurrency, partition strategy
Cache Tables
Security
Sentry Integration
Kerberos Ticket Renewal
Navigator Integration

17. Implementation Rapidly Changing Technology
Feature addition
Documentation
Bugs
Jar hell
Compression Codec for Parquet
S3 Eventual Consistency
Small files
Performance Issues
Compaction

18. Implementation Partition Strategy Parquet Files
Balancing parallelism and throughput
Table Partitions
Cluster sizing, optimization and tuning
Integrating with Corporate infrastructure
Deployment practices
Monitoring and Alerting
Information Security Policies

19. Enabling predictive analytics and real-time decisions
Value Add
Enabling predictive analytics and real-time decisions
Integrated Scorecards – Daily /Weekly / Monthly Insights
Near Real Time / Hourly / Daily Insights
Multivariate Testing, APT (Test vs. Control Analysis), and Text Analytics
Testing for Both Hotel and Customer / Research For Guest Insights
Personalized Display Ad Serving
Real-time Actions (Machine Learning) Across Guest Touch Points
Hotel Lifecycle Data
Real-time Alerts for Hotel Related Actions
Strata Hadoop World | New York City | September 29th, 2016

20. CLAIRVOYANT BACKGROUND AWARDS & RECOGNITION
One of the fastest growing big data companies
Extensive experience in providing strategic and architectural consulting on Big Data platforms and implementations
Global delivery experience across multiple locations in US, Asia and Latin America
100+ big data experts worldwide - US, Latin America and Asia
AWARDS & RECOGNITION
C L A I R V O Y A N T S O F T . C O M

21. We are hiring! http://careers.choicehotels.com/careers.html
Strata Hadoop World | New York City | September 29th, 2016