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Guidance for Real-Time Casino Player Analytics on AWS

Overview

This Guidance shows how your developers can build a real-time analytics pipeline that uses AI to deliver effective marketing offers during game sessions. By using gaming-machine and shuffler data to update machine learning (ML) models in real time, this pipeline predicts the best offers for individual customers. The analytics pipeline then returns these findings to your gaming machines and applications so that you can promote offers based on each user’s customer profile.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

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.

Amazon CloudWatch enhances observability through metrics and helps you visualize data through personalized dashboards and logs. Additionally, X-Ray helps you analyze user requests as they travel through your API Gateway APIs to the underlying services. Together, CloudWatch and X-Ray can help you identify performance bottlenecks and troubleshoot requests.

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In this Guidance, devices use X.509 certificates for authentication and AWS IoT policies for authorization so that they can securely connect with each other, AWS IoT Core, and IoT Greengrass. Additionally, all AWS Identity and Access Management (IAM) policies have been scoped down to the minimum permissions required for the services to function properly, helping you limit unauthorized access to resources.

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API Gateway and Lambda operate in multiple Availability Zones (AZs) in each AWS Region, using this redundancy to maintain availability even in the case of infrastructure failure. API Gateway automatically recovers from the failure of an AZ. Additionally, Kinesis Data Streams provides a default 24-hour retention period, enabling you to select a specific timestamp from which to start processing records. This helps you reliably resume processing at a later time without data loss. Finally, DynamoDB provides on-demand backup capability, point-in-time recovery, and global tables that sync across Regions to help support your data resiliency and backup needs.

Read the Reliability whitepaper 

Kinesis Data Streams enables multiple applications to consume data from the same stream. As a result, multiple actions, like archiving and processing, can take place concurrently and independently, providing higher throughput. CloudWatch provides actionable insights that help you optimize application performance, manage resource utilization, and understand system-wide operational health. Finally, Amazon DynamoDB Accelerator (DAX) helps you increase performance by providing quicker response times through in-memory reads.

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The DynamoDB automatic scaling feature manages throughput based on your application traffic and your target utilization metric. This helps you make sure your tables have the required capacity required for your application and helps you avoid the cost of overprovisioning. Amazon S3 also provides automatic scalability, helping you increase your agility. Together, these services lower the total cost of ownership for storing and retrieving data.

Read the Cost Optimization whitepaper 

Lambda allows you to run code without provisioning or managing servers, and its functions automatically scale to meet the demand. It also reuses implementation environments, improving your resource utilization. These capabilities help you optimize the resource usage of your application, minimizing the energy consumption of your workloads.

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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.