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How Data Drives Business at Choice Hotels

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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

Publicada em: Software
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How Data Drives Business at Choice Hotels

  1. 1. 1© Cloudera, Inc. All rights reserved. Webinar: How Data Drives Business at Choice Hotels Cloudera & Choice Hotels
  2. 2. 2© Cloudera, Inc. All rights reserved. Our Speakers Today… Vijay Raja Sr. Solutions Marketing Manager Cloudera Robert Bushman Principal Software Engineer Choice Hotels
  4. 4. 4© Cloudera, Inc. All rights reserved. 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)
  5. 5. 5© Cloudera, Inc. All rights reserved. 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)
  6. 6. 6© Cloudera, Inc. All rights reserved.
  7. 7. 7© Cloudera, Inc. All rights reserved. FILESYSTEM RELATIONAL Cloudera Enterprise Data Platform CLOUDERA ENTERPRISE
  8. 8. 8© Cloudera, Inc. All rights reserved. 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. 9. Big Data Success Stories: Choice Hotels and Cloudera
  10. 10. About Choice Hotels 6400 Hotels in 40 Countries Around The World
  11. 11. Use Cases
  12. 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. 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. 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. 15. Swiss Army Knife • PCI Compliance • SOX Audits • Historical Views • BI Discovery • Geo-Trends • Glue
  16. 16. System Overview
  17. 17. Platform Architecture IMPALA
  18. 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. 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. 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. 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. 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. 23. Lessons Learned
  24. 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. 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!
  26. 26. 26© Cloudera, Inc. All rights reserved. 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
  27. 27. 27© Cloudera, Inc. All rights reserved. Customers Across All Industries Other Financial Services Telecom Healthcare & Life Sciences Public Sector & Education
  28. 28. 28© Cloudera, Inc. All rights reserved. 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.
  29. 29. 29© Cloudera, Inc. All rights reserved. Thank you