Use technologies that support data access and storage patterns

Utilizing the right storage technologies is crucial to minimizing resource consumption while effectively supporting workload requirements. Efficient data access and storage patterns can lead to reduced carbon footprints and enhanced sustainability by optimizing resource allocation.

Best Practices

Optimize Storage Based on Access Patterns

• Analyze your data access patterns to determine the best storage solution for each data type and usage scenario. This helps reduce unnecessary resource consumption.
• Implement tiered storage solutions, such as using Amazon S3 for infrequently accessed data and Amazon EBS for high-performance needs, to match storage type with usage and reduce costs.
• Use lifecycle policies to automatically transition data to less expensive storage options or delete obsolete data, freeing up resources and minimizing environmental impact.
• Regularly audit and review storage usage to ensure alignment with current requirements and to identify opportunities for further optimization.
• Choose serverless or managed storage services where appropriate, as they often provide automatic scaling and can reduce the operational burden and waste.

Questions to ask your team

• What storage technologies are currently in use, and how do they align with your data access patterns?
• Have you classified your data to understand its business value and usage frequency?
• Are you leveraging any tiered storage solutions that allow for data movement based on access frequency?
• How often do you review your data management policies to ensure they support sustainability goals?
• Do you have automated processes in place for data lifecycle management, including archiving and deletion of unnecessary data?
• What metrics are you tracking to assess the effectiveness of your data management practices on sustainability?

Who should be doing this?

Data Architect

• Design data storage solutions that optimize both performance and sustainability.
• Select and implement storage technologies that align with data access patterns.
• Evaluate and recommend efficient data lifecycle management policies.
• Collaborate with engineering teams to understand data usage patterns and requirements.

Cloud Operations Engineer

• Monitor and manage provisioned storage resources to ensure efficiency.
• Implement data management policies to automatically transition data to less performant storage when appropriate.
• Ensure compliance with data retention and deletion policies to minimize unnecessary storage use.
• Review and optimize cloud resource usage regularly for sustainability improvements.

Data Analyst

• Analyze data usage patterns to provide insights on optimal storage configurations.
• Assist in identifying data that can be archived or deleted based on business needs.
• Collaborate with stakeholders to ensure data management practices align with sustainability goals.

Compliance Officer

• Ensure that data management practices comply with relevant regulations and sustainability standards.
• Review data governance policies to confirm they support sustainability objectives.
• Educate teams on compliance requirements related to data storage and management.

What evidence shows this is happening in your organization?

• Data Storage Policy Template: A standardized template for defining data storage guidelines, including capacity planning, data classification, and retention policies to minimize unnecessary resource usage.
• Data Usage Monitoring Dashboard: A real-time dashboard that tracks storage consumption, data access patterns, and cost metrics, enabling quick adjustments to optimize sustainability.
• Storage Technology Selection Guide: A reference guide to help teams choose the most appropriate storage services and configurations based on access frequency, data volume, cost, and sustainability objectives.
• Data Lifecycle Management Plan: A plan outlining policies and schedules for transitioning data to lower-cost, more sustainable storage services and for securely disposing of data that is no longer needed.
• Auto-Tiering Optimization Strategy: A strategy document explaining how to configure automatic tiering for data, ensuring that resources are efficiently allocated and storage layers align with actual usage patterns.

Cloud Services

AWS

• Amazon S3: Provides scalable object storage with lifecycle policies to transition data to lower-cost storage tiers and automatically delete data that is no longer needed.
• Amazon Redshift: A data warehouse service that enables efficient data storage and retrieval patterns, allowing for optimized storage configurations.
• AWS Storage Gateway: Integrates on-premises environments with AWS cloud storage, helping to optimize data storage management and reduce resources provisioned.

Azure

• Azure Blob Storage: Offers scalable object storage with tiered access levels and lifecycle management policies for optimizing storage costs and performance.
• Azure Data Lake Storage: Designed for high-scale analytics workloads, it optimizes data storage patterns and configurations for better resource usage.
• Azure Archive Storage: Provides cost-effective storage for rarely accessed data, reducing provisioning and maximizing sustainability.

Google Cloud Platform

• Google Cloud Storage: Offers different storage classes that help manage data lifecycle and access patterns, minimizing costs and resource usage.
• BigQuery: A serverless data warehouse that optimizes data storage, retrieval, and query performance, ensuring efficient resource use.
• Google Cloud Datastore: A NoSQL document database that automatically handles scaling and resource optimization, providing efficient data access patterns.