This Guidance helps customers bring together different types of datasets and merge them into a single, consolidated view. AWS Game Tech customers can create a thorough behavioral profile of their players to gain further insights into how players interact with a game, participate within the game community, and socialize with other players. The Cohort Modeler categorizes and aggregates player metrics into individual player groupings, based on different types of metric data, including in-game metrics, in-game behaviors, and financial transactions. Deeper understanding into player behavior informs ongoing design and development decisions.

Architecture Diagram

[Architecture diagram description]

Download the architecture diagram PDF 

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.

  • Application, workload, and infrastructure component telemetry is accessed through Amazon CloudWatch Logs. All operational health metrics are accessible through CloudWatch. The application itself tracks user and transaction telemetry through ingest and query APIs. 

    Read the Operational Excellence whitepaper 
  • All data resides in Neptune and is encrypted at rest. Any bulk ingestion data (non-API data) resides on Amazon Simple Storage Service (Amazon S3) and is also encrypted at rest. Data in-transit is encrypted through a dedicated VPC endpoint to which only Neptune has access. Any query data (through the API) is encrypted in-transit using Transport Layer Security (TLS)/HTTPS.

    Read the Security whitepaper 
  • The architecture is decoupled using a three-tier access pattern, from API Gateway to Lambda to Neptune. Each layer is independently scalable and highly available. Additionally, the layers are stateless and allow for automatic retry limits. Each layer individually sends logs to CloudWatch for analysis. The architecture is delivered as infrastructure code (IaC) through CloudFormation. CloudFormation manages any updates, roll-backs, or errors.

    Read the Reliability whitepaper 
  • The services in this architecture provide automatic scaling and linear cost forecasting. Neptune has features to explore and determine player and cohort relationship modeling. The architecture also uses a reference Jupyter Notebook with code samples and provides step-by-step instructions on ingesting, querying, and modeling data

    Read the Performance Efficiency whitepaper 
  • The architecture minimizes data transfer costs out of the AWS Region by charging for API query responses for player insights only. This results in data transfer costs only being incurred for services used in the architecture and not for data ingest. Additionally, you can forecast costs based on past usage. 

    Read the Cost Optimization whitepaper 
  • The services in this solution are serverless, which eliminates the need for hardware. In general, Neptune supports serverless capabilities. In this architecture, we use a version of Neptune that is not serverless but that still uses the minimum amount of hardware needed to maintain reliability. 

    Read the Sustainability whitepaper 

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.

AWS for Games

Gain Insights Into Your Player Base Using The AWS for Games Cohort Modeler

This is the first blog post in a series focusing on the AWS for Games Cohort Modeler. 
The AWS for Games Cohort Modeler is a deployable solution for developers to map out and classify player relationships and identify like behavior within a player base. 
AWS for Games

AWS for Games Cohort Modeler: Graph Data Model

This is the second blog of the series introducing the AWS for Games Cohort Graph.
This blog post dives deeper into the data model and demonstrates how customers can adapt the example schema to the specific needs of their studio.


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.

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