1 4 3 2 1 Revenue Management Architecture for Lodging Migrate an on-premises revenue management system to Amazon Web Services (AWS) to add near real-time capabilities, improve the flexibility and agility of reporting and analytics, and reduce the total cost of ownership (TCO) by using Spot Instances and bigdata architecture. Tiered data lake architecture using Amazon Simple Storage Service (Amazon S3) helps you ingest and process data from a variety of batch and near real-time data feeds. In addition, this architecture helps you add new data feeds and propagate data changes easier. Existing revenue management modules can be migrated to useAmazon Elastic Compute Cloud (Amazon EC2) Spot Instances to significantly reduce the cost of infrastructure without making any changes to the code. Amazon Elastic File System(Amazon EFS)replicates the file structure and files required by the modules. Outputs from the revenue management modules are converted and stored in the data lake to facilitate the reporting and analytics of the optimized data. Enable near real-time booking controls with Amazon EC2 On-Demand Instances to adjust booking and pricing controls. Flexible and on-demand reporting is provided by using the data lake that has all the raw, processed, and optimized data, and using a combination of Amazon Redshiftand Amazon Athena. Use Amazon Forecast to create regional demand forecasting models and Amazon SageMaker for advanced revenue analytics. Build a revenue management dashboard to use reporting and analytics capabilities and allow for adjustments to configurations and user overrides. 5 lodging systems • property data (inventory, room numbers, room types, attributes) • CRS (reservations) • PMS (stays, folios, and ancillaries) • Point of Sale (ancillaries) • group booking system • booking engine, web and mobile analytics (shopping data) •market data and competitive data: KeyDataDashboard, Phocuswright, Smith Travel Research (STR) • shopping data: BlueTent, InterCoastal Net Designs (ICND), Q4Launch, Scurto Marketing, Travel Click external systems AWS Cloud Amazon S3 • property data • competitor data • shopping data (indirect channels) •market data batch staging • reservations • stays • shopping data (direct channels) • folios • ancillaries near real-time staging • property data • reservations • stays and folios • ancillaries • shopping data • competitor data •market data processed • overbooking policies • oversale costs • property capacities • spoilage costs • variable costs • holiday and event calendar • user overrides configurations transform AWS Data Exchange AWS Transfer Family AWS Glue Amazon EMR AWS Glue Data Catalog events property management systems (PMSs) Amazon Kinesis data events • demand forecasts • show up forecasts • overbooked capacity • optimized pricing • post processed booking and pricing controls optimized Amazon DynamoDB rev. mgmt. configurations AWS Step Functions microservices Amazon API Gateway 1 Revenue management systems implemented onpremises cannot scale to meet the demands of the business to add real-time data feeds and dynamic adjustments to booking controls, and to meet the changing and on-demand reporting and analytics needs. On-premises infrastructure and data platforms cannot meet these requirements without significant increase in costs and resources. This migration architecture builds upon the foundation of Data Platform for Lodgingthat provid
