Ensure essential store functions like point-of-sale and security systems operate reliably, even during network interruptions. Local processing capabilities keep your business running while automatically synchronizing with the cloud when connectivity returns.
Overview
This Guidance demonstrates how to enhance customer experience and operational efficiency in retail by combining AWS edge computing services with on-premises retail facilities. IoT sensors, cameras, and point-of-sale systems collect in-store data that's processed locally through AWS IoT Greengrass and edge components for immediate analysis. The system enables real-time customer journey optimization through Amazon Bedrock Agents and Amazon Personalize while maintaining security with Amazon GuardDuty. You can streamline operations, deliver personalized shopping experiences, and make data-driven decisions faster without compromising on security or performance
Benefits
Maintain critical retail operations
Deliver real-time personalized experiences
Process customer data instantly at store locations to provide timely, personalized recommendations and experiences. Combine edge analytics with cloud-based AI to optimize customer journeys and increase engagement.
Reduce operating costs
Reduce data transfer and infrastructure costs by processing store data locally and sending only relevant information to the cloud. Maximize existing hardware investments through containerized applications.
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.
Step 1
IoT sensors, IP cameras, point-of-sale systems, mobile clients, and appliances collect raw data in-store
Step 2
AWS IoT Greengrass streams data via Device Gateway and Stream Manager
Step 3
Data is initially processed by edge components using Lambda function and AI/ML Inference for quick analysis
Step 4
Amazon EKS Hybrid Node/Amazon ECS Anywhere runs Applications & Databases that support Point of Sale and Retail Applications
Amazon GuardDuty provides threat detection at the edge
Step 6
App data, logs, and metrics are sent to Amazon Kinesis, Amazon EventBridge, or IoT Topic via MQTT, HTTP, or WebRTC protocols
Step 7
Data is stored in Amazon Kinesis, Amazon S3 or managed database where transformation is conducted with AWS Glue or AWS Lambda. Store Analytics surfaced using Amazon Athena.
Step 8
Machine Learning is conducted and edge models optimized using Amazon SageMaker AI
Step 9
Amazon Bedrock Agents & Amazon Personalize optimize customer journeys and personalized recommendations
Amazon Location Service kicks off business logic when customer enters/exits store Geofence
Step 11
Content is updated and distributed globally with Amazon Cloudfront to closest Edge location. Lambda@Edge allows auth logic to be added & AWS WAF provides protection from exploits
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