The AWS services used for this Guidance provide you with a comprehensive, cloud-native framework to extend your on-premises Ignition deployments. By treating infrastructure as code, using scalable data and analytics services, and enabling edge machine learning capabilities, you can enhance operational visibility, responsiveness, and optimization across your industrial environments.

This Guidance, when deployed on AWS, uses several services to enhance your overall security posture. These include AWS Identity and Access Management (IAM) for controlling access, AWS Key Management Service (AWS KMS) for protecting data, an d AWS IoT Core for secure communication. These services work in concert to fortify the deployment with robust access control, data protection, and secure connectivity throughout the Guidance.

Elastic Load Balancing (ELB) routes traffic requests from users' desktop and mobile applications to only the healthy Amazon Elastic Compute Cloud (Amazon EC2) instances, so that traffic is not directed to instances that are nearing overload. This approach reduces the likelihood of application failure, allowing users to seamlessly browse the mobile storefront without encountering downtime errors.

Aurora , configured in a multi-Availability Zone (multi-AZ) deployment, provides a highly available and fault-tolerant database infrastructure so that the Ignition SCADA system can continue operating efficiently. Additionally, the Application Load Balancer is employed to distribute traffic across multiple Ignition frontend instances so that the system can handle increased loads and traffic patterns efficiently. 

Amazon S3 is used for data storage, as it offers a range of storage classes , including Standard, Infrequent Access, and Glacier, among others, allowing for the optimization of costs based on the specific data access patterns and durability requirements of the Guidance. The multi-AZ deployment of Aurora is used for its capability to scale the database tier up or down based on demand, thereby avoiding over-provisioning and minimizing costs.

SageMaker is a fully managed service for building, training, and deploying machine learning models, allowing you to optimize resource utilization and minimize the environmental impact associated with manual processes and inefficient resource management. Amazon S3 offers different storage classes that are optimized for various data access patterns and durability requirements so you can minimize the resources needed for data storage and reduce the associated environmental impact.