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This Guidance demonstrates secure data collaboration for travel and hospitality (T&H) brands. For T&H brands to collaborate, they must have a way to securely share data with one another. AWS Clean Rooms supports secure data transfer, without transfer of data out of respective AWS accounts. Once this data has been shared, collaborating brands can offer joint, personalized offers and promotions to guests or travelers through up-selling and cross-selling, improving overall customer engagement.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Data providers (brand partners) catalog their guest/traveler interactions, events, loyalty, and preferences metadata stored on Amazon Simple Storage Service (Amazon S3) and use AWS Glue Data Catalog for collaboration with other T&H brands in AWS Account A.
Step 2
With a data consumer catalog, first-party guest/traveler profile metadata is stored on Amazon S3, and the data consumer can use AWS Glue Data Catalog to accept collaboration from other T&H brands in AWS Account B.
Step 3
The data consumer in AWS Account B creates a principal AWS Clean Rooms collaboration with a querying capability using an AWS Glue Data Catalog table and extends the invitation for collaboration to data providers.
Step 4
Data providers accept the invitation from the data consumer to JOIN principal collaboration in AWS Clean Rooms and configure their AWS Glue Data Catalog table in AWS Account A.
Step 5
Use AWS Clean Rooms query editor in AWS Account B to join guest/traveler profiles with their brand interactions, preferences, and events data in AWS Account A, and store the results on the Amazon S3 staging bucket.
Step 6
Use AWS Glue to catalog, transform, and aggregate guest/traveler profiles with their preferences, brand likings, and interactions, and store the data back to Amazon S3 in a transformed bucket.
Step 7
Amazon Personalize uses the transformed data stored on Amazon S3 to build a personalized promotion recommendation model based on guest/traveler profile preferences.
Step 8
Configure Amazon Pinpoint to integrate Amazon Personalize promotions campaigns, and activate push notifications, email, and SMS.
Step 9
Optionally, build guest/traveler profile preferences reports on Amazon Redshift with data stored on Amazon S3. Use Amazon QuickSight to build visual dashboards based on those reports.
Step 1
Data providers (brand partners) catalog their guest/traveler interactions, events, loyalty, and preferences metadata stored on Amazon Simple Storage Service (Amazon S3) and use AWS Glue Data Catalog for collaboration with other T&H brands in AWS Account A.
Step 2
With a data consumer catalog, first-party guest/traveler profile metadata is stored on Amazon S3, and the data consumer can use AWS Glue Data Catalog to accept collaboration from other T&H brands in AWS Account B.
Step 3
The data consumer in AWS Account B creates a principal AWS Clean Rooms collaboration with a querying capability using an AWS Glue Data Catalog table and extends the invitation for collaboration to data providers.
Step 4
Data providers accept the invitation from the data consumer to JOIN principal collaboration in AWS Clean Rooms and configure their AWS Glue Data Catalog table in AWS Account A.
Step 5
Use AWS Clean Rooms query editor in AWS Account B to join guest/traveler profiles with their brand interactions, preferences, and events data in AWS Account A, and store the results on the Amazon S3 staging bucket.
Step 6
Use AWS Glue to catalog, transform, and aggregate guest/traveler profiles with their preferences, brand likings, and interactions, and store the data back to Amazon S3 in a transformed bucket.
Step 7
Amazon Personalize uses the transformed data stored on Amazon S3 to build a personalized promotion recommendation model based on guest/traveler profile preferences.
Step 8
Configure Amazon Pinpoint to integrate Amazon Personalize promotions campaigns, and activate push notifications, email, and SMS.
Step 9
Optionally, build guest/traveler profile preferences reports on Amazon Redshift with data stored on Amazon S3. Use Amazon QuickSight to build visual dashboards based on those reports.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
You can use Amazon CloudWatch with this Guidance, which provides service-wide visibility through logs, metrics, and alarms to proactively monitor your applications and infrastructure. This helps you improve operational efficiency of your architecture by responding to events in near real time.
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SecurityRead the Security whitepaper
AWS Clean Rooms provides an isolated, secure collaboration environment to analyze and combine sensitive data without moving data out of respective AWS Accounts. This service helps you share your sensitive data with data partners or providers, without exposing raw data. AWS Clean Rooms provides built-in cryptographic encryption and access control mechanisms that prevent unauthorized access to sensitive data outside of your organization.
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ReliabilityRead the Reliability whitepaper
Amazon Personalize uses machine learning (ML) to help you build a recommendation model that auto-scales resources based on demand so you don’t have to worry about infrastructure reliability. Automatically adjusting to demand increases the reliability of your application during peak hours.
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Performance EfficiencyRead the Performance Efficiency whitepaper
Amazon Redshift decouples compute and storage to optimize query performance as data volumes grow. Amazon Redshift provides fast query performance through its massively parallel processing architecture, columnar data storage, and advanced query optimization. This helps ensure timely reporting of complex data queries.
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Cost OptimizationRead the Cost Optimization whitepaper
Amazon S3 provides cost-effective storage by transitioning objects to lower-cost tiers, such as S3 Infrequent Access or Amazon S3 Glacier. This service provides object lifecycle management to automatically transition less frequently accessed data to more affordable tiers, minimizing overall storage expenses of your architecture.
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SustainabilityRead the Sustainability whitepaper
AWS Glue is an extract, transform, load (ETL) service that supports autoscaling of resources based on demand, eliminating the need to maintain instances for your workflows. This reduces the chances of over-provisioning or under-provisioning resources, supporting overall sustainability of your architecture.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.