# MIDASUS03-BP01 Implement edge data and cross-region movement strategies
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 Implement strategies that process information at the edge when possible and strategically manage cross-Region transfers in manufacturing environments. This approach reduces unnecessary network traffic, lowers energy consumption, and improves operational efficiency in factory environments. 

 **Desired outcome:** Reduced data transfer across networks, optimized energy usage, faster access to manufacturing data, and improved application performance with lower carbon impact. 

 **Benefits of establishing this best practice:** 
+  Decreased network bandwidth consumption and associated energy usage 
+  Reduced carbon footprint from data centers and networking equipment 
+  Lower latency for manufacturing applications requiring real-time data 
+  Cost savings from reduced data transfer fees 

 **Level of risk exposed if this best practice is not established:** Medium 

## Implementation guidance
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+  Process data at the source or edge locations to filter, compress, or aggregate data before transmitting to centralized storage, reducing unnecessary data movement and processing requirements. 
+  Implement efficient data transfer mechanisms when cross-region or cross-zone data movement is necessary, using compression, batching, and optimized transfer strategies. 
+  Store data in locations geographically closest to where it will be processed and accessed most frequently to minimize network latency and reduce energy consumed during data transit. 
+  Apply data lifecycle management strategies to automatically tier, archive, or delete data based on access patterns, compliance requirements, and business value, reducing storage footprint and associated energy costs. 

### Implementation steps
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1.  **Edge processing implementation:** 
   +  Deploy AWS IoT Greengrass to process sensor data locally at the edge 
   +  Configure data filtering rules to send only aggregated results to the cloud 
   +  Set up Lambda functions for edge-based data processing and reduction 

1.  **Efficient data transfer configuration:** 
   +  Implement Amazon S3 Transfer Acceleration and AWS Global Accelerator for cross-region data movement 
   +  Use Amazon CloudFront to cache frequently accessed data closer to end users 

1.  **Geographic data optimization:** 
   +  Store data in AWS Regions closest to production facilities 
   +  Configure Amazon S3 lifecycle policies for efficient data management 
   +  Monitor data access patterns using CloudWatch to identify optimization opportunities 

## Key AWS services
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+  AWS IoT Greengrass 
+  Amazon S3 Transfer Acceleration 
+  AWS Global Accelerator 
+  Amazon CloudFront 

## Resources
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+  [AWS IoT Greengrass](https://docs.aws.amazon.com/greengrass/latest/developerguide/what-is-gg.html) 
+  [Configuring fast, secure file transfers using Amazon S3 Transfer Acceleration](https://docs.aws.amazon.com/AmazonS3/latest/userguide/transfer-acceleration.html) 
+  [AWS Global Accelerator ](https://docs.aws.amazon.com/global-accelerator/latest/dg/what-is-global-accelerator.html) 
+  [AWS for the Edge](https://aws.amazon.com/edge/)