SUS04-BP06 - Use shared file systems or storage to access common data
Implementation Guidance
“Use shared file systems or storage to access common data” aligns people, process, and communication so operational execution remains predictable under pressure. Define responsibilities explicitly and validate that teams can execute procedures during real events.
For the question “How do you take advantage of data access and usage patterns to support your sustainability goals?”, define measurable outcomes, assign owners, and review execution regularly. Integrate this practice into delivery and operations processes so improvements persist as workloads and requirements evolve.
Key Steps
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Define communication and ownership model:
- Clarify who is responsible for executing “Use shared file systems or storage to access common data”
- Document escalation paths and decision authority boundaries
- Standardize communication templates for operational events
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Enable teams with repeatable practices:
- Create runbooks, checklists, and onboarding materials
- Train teams through drills, simulations, or tabletop exercises
- Validate that procedures can be executed under time pressure
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Measure effectiveness and adapt:
- Track response quality, handoff quality, and operational lead time
- Address recurring coordination gaps with process updates
- Share lessons learned and improvements across teams
Risk / Impact
Level of risk if not implemented: Medium
Impact: Without this best practice, workloads typically accumulate inefficiencies and execution drift that increase failure probability over time. Problems often surface during traffic spikes, major releases, or dependency failures.
Benefits of implementation:
- More predictable operational and engineering outcomes
- Better alignment between architecture decisions and business goals
- Continuous improvement through measurable feedback loops
AWS Services to Consider
Amazon S3
Delivers durable object storage with lifecycle controls for efficient data management.
AWS Glue
Automates data cataloging and ETL workflows for efficient data processing.
Amazon Athena
Queries data in S3 with serverless SQL for analytics and reporting.
Amazon EMR
Runs scalable big data processing frameworks for batch and streaming workloads.