This Guidance helps you accelerate your product development lifecycle by using AWS as the foundation for a model-based systems engineering (MBSE) approach to engineering and design. MBSE allows you to securely transform traditional document-based engineering environments to a modern model-based cloud computing platform. This multi-disciplinary and multi-application model helps aerospace companies adopt agile product development practices, connect with other aerospace teams across the globe, and gain the the support they need at every stage of MBSE adoption and automation. 

Architecture Diagram

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.

  • Whether you are just starting out with MSBE as a tool or putting MSBE at the center of your enterprise strategy, this architecture provides the flexibility for you to get started. You can use Option A to incorporate MSBE into your existing environment or use tools like SOCA to centralize MSBE. The services in this architecture and the data lake approach enable centralized management and visibility for IT and security teams. Additionally, the architecture uses data analytics services to generate insights for engineering teams, so they can forecast how changes will impact larger systems.  

    Read the Operational Excellence whitepaper 

  • This architecture uses AWS Identity and Access Management (IAM) and Amazon CloudWatch to protect data. IAM provides role-based access control, giving data access privileges to only the roles that need it. With CloudWatch, you can set up metrics to monitor application activity from multiple AWS accounts within a Region. 

    Read the Security whitepaper 

  • This architecture uses a microservices approach, which decouples services for a particular engineering function from services that support a different engineering function. By decoupling these services, you can experiment with new technologies for one function without altering the operability of other functions. The services in the Human-Machine Engineering Workflow capture, document, and respond to all events, maintaining a single “source of truth” for all event-based activity and communications.

    Read the Reliability whitepaper 

  • The services in this architecture allow for data interoperability across multiple stages of the data lifecycle. The AWS Management Console gives you visibility into data access patterns of your data, such as requests or changes to data and velocity or size of data. You can then build business logic based on traffic patterns and execute the logic with extensible APIs.

    Read the Performance Efficiency whitepaper 

  • This architecture uses cost-saving features such as automation through CodePipeline, scalability through Amazon S3, and centralized administration through AWS Organizations. These features allow for early detection and correction of defects in the design process, which reduces total development costs and schedule overruns. 

    Read the Cost Optimization whitepaper 

  • This architecture uses services that scale resources up and down based on usage. These services help monitor the throughput of the file system and dynamically adjust the throughput mode to “provisioned” or “bursting” to maximize resource optimization. With the “Detective” services in this architecture, you can visualize productivity metrics, emissions, or cost-out targets through dashboards and adjust business priorities to meet target metrics for sustainability. 

    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.

Open implementation guide
Open sample code on GitHub

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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.

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