MIDASUS03-BP01 Implement edge data and cross-region movement strategies
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:
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Decreased network bandwidth consumption and associated energy usage
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Reduced carbon footprint from data centers and networking equipment
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Lower latency for manufacturing applications requiring real-time data
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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.
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Implement efficient data transfer mechanisms when cross-region or cross-zone data movement is necessary, using compression, batching, and optimized transfer strategies.
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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.
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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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Edge processing implementation:
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Deploy AWS IoT Greengrass to process sensor data locally at the edge
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Configure data filtering rules to send only aggregated results to the cloud
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Set up Lambda functions for edge-based data processing and reduction
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Efficient data transfer configuration:
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Implement Amazon S3 Transfer Acceleration and AWS Global Accelerator for cross-region data movement
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Use Amazon CloudFront to cache frequently accessed data closer to end users
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Geographic data optimization:
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Store data in AWS Regions closest to production facilities
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Configure Amazon S3 lifecycle policies for efficient data management
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Monitor data access patterns using CloudWatch to identify optimization opportunities
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Key AWS services
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AWS IoT Greengrass
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Amazon S3 Transfer Acceleration
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AWS Global Accelerator
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Amazon CloudFront
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