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This Guidance shows how to integrate your industrial data fabric with Siemens Industrial Edge and AWS to analyze industrial asset data at scale. Industrial Edge is an open, ready-to-use edge computing platform that features a centralized management system for many Industrial Edge apps and a variety of devices. Using AWS IoT SiteWise Edge to integrate AWS with Industrial Edge, you can use preprocessed operational data with Industrial Edge apps and AWS services. This enables you to promote industrial connectivity, contextualization, analytics, AI, automation, and more, empowering data-driven decision-making.
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Architecture Diagram
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Step 1
Siemens Industrial Edge is an open software platform for simple, scalable, and manageable shop-floor IT. It provides decentralized and local data acquisition, storage, analytics, AI, and connectivity to AWS. The Industrial Edge Management application enables remote and central management of edge devices and applications.
Get Started
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Well-Architected Pillars
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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 Excellence
This Guidance uses various managed services that lower your operational overhead by letting you focus on your business instead of managing your IT infrastructure. For example, AWS IoT SiteWise, Amazon Managed Grafana, Amazon S3, and Athena help you avoid the heavy lifting of installing and maintaining operating systems and software. These services also integrate with Amazon CloudWatch or AWS CloudTrail to enhance observability. Additionally, AWS IoT SiteWise Edge, which is easily installable on Industrial Edge, gathers metrics to help you gain insights into your edge deployment.
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Security
This Guidance uses services that provide built-in security and support encryption in transit and at rest. CloudWatch and CloudTrail support traceability, and AWS Identity and Access Management (IAM) helps you implement a strong identity foundation.
As part of the central system, Siemens Industrial Edge Management makes sure that only users with the right permissions can access and perform changes. It also enables you to remotely upgrade the full software stack of your devices, starting with the firmware. Certified by the Cloud Security Alliance, Industrial Edge Management encrypts communications to other systems and cloud services and securely stores credentials and secrets. Refer to Security Best Practices for Manufacturing OT for more information on designing, deploying, and securing distributed manufacturing workloads and resources at the industrial edge.
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Reliability
This Guidance includes AWS managed services like AWS IoT Core, AWS IoT SiteWise, Amazon S3, and Athena, which are scalable and highly available by design. They run on the AWS global infrastructure, which is designed to provide low-latency, high-throughput, and highly redundant networking. You can monitor your service quotas and ask for an increase if needed to match demand. Additionally, AWS IoT SiteWise Edge on Industrial Edge buffers data before it is ingested into AWS, helping resolve intermittent connectivity issues.
As the central system, Industrial Edge enables you to easily manage remote backup-and-restore operations of your edge deployments. Additionally, Siemens Industrial Edge Management does not require uninterrupted internet connectivity, and you can deploy it in a highly available Kubernetes infrastructure.
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Performance Efficiency
AWS IoT SiteWise provides hot, warm, and cold storage tiers so that it can support both real-time and batch processing. AWS IoT TwinMaker and AWS Managed Grafana provide real-time data visualization, while Amazon QuickSight, Athena, and Amazon S3 support batch processing. Finally, SageMaker enables you to automatically scale and adjust the compute resources used for your ML workloads based on demand.
Industrial Edge provides real-time data processing at the edge, reducing latency and facilitating timely decision-making. Additionally, it lets you centralize the management of connected devices and software. This makes deployment and application updates easier and more efficient across multiple machines and plants. Finally, Industrial Edge employs a microservice approach, so you only need to deploy the services or applications required for the task at hand while having the flexibility to deploy further functionality to the same devices later. This enables you to rightsize your devices, enhancing performance efficiency.
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Cost Optimization
This Guidance uses managed services that reduce the cost of infrastructure management and provisioning. These services also only charge based on the resources you use, with no upfront costs or long-term usage commitments. SageMaker also automatically scales resources up and down based on demand. Additionally, you can use AWS Organizations and AWS Budgets to measure overall cost efficiency and learn how to use AWS services more cost efficiently. For example, Athena supports data partitioning, which enables you to reduce costs by scanning only a subset of your data. And Amazon S3 Intelligent-Tiering and AWS IoT SiteWise provide different storage tiers so that you can store data cost effectively based on access patterns. AWS IoT SiteWise also lets you ingest multiple properties in one API call, lowering data ingestion costs.
Additionally, Industrial Edge lets you define a use case once and deploy it across multiple devices, avoiding the cost of individual device setup. This approach also enables centralized management and updates, reducing the time and effort required for maintenance. And by supporting local data processing at the edge—closer to data sources—Industrial Edge helps you significantly reduce data transfer and cloud storage costs. This helps you run computational resources more efficiently and avoid the need for extensive IT infrastructure, minimizing engineering efforts and ultimately lowering expenses.
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Sustainability
This Guidance uses managed AWS services to maximize resource utilization and avoid overprovisioning. Through the scale and efficiency of the AWS global infrastructure compared to on-premises data centers, you can greatly reduce your carbon footprint and energy consumption. Additionally, many AWS services are designed for sustainability, incorporating features like energy-efficient hardware, renewable energy sources, and efficient resource utilization to minimize the environmental impact. You can use the Customer Carbon Footprint Tool to track, measure, review, and forecast the carbon emissions generated from your AWS usage so that you can optimize your energy use.
Industrial Edge also significantly contributes to sustainability through its energy efficiency and resource optimization. By processing data locally at the edge, it avoids extensive data transmission to central servers, lowering your energy consumption and reducing your carbon footprint. It also enables real-time monitoring and optimization of industrial processes, leading to more efficient use of resources such as water, raw materials, and energy.
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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.