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SUS04

SUS04-BP08 - Back up data only when difficult to recreate

Implementation Guidance

“Back up data only when difficult to recreate” should be delivered as a standard operating capability with explicit scope, controls, and validation checkpoints. Embed it into day-to-day engineering and operations workflows.

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

  1. Define implementation scope and outcomes:

    • Set explicit success criteria for “Back up data only when difficult to recreate”
    • Identify dependencies, prerequisites, and sequencing constraints
    • Assign accountable owners for execution and maintenance
  2. Implement with standards and validation:

    • Use reusable templates and runbooks for consistent execution
    • Validate implementation with tests, checks, or controlled rollouts
    • Capture telemetry to confirm adoption and effectiveness
  3. Operate and iterate:

    • Review outcomes against KPIs on a recurring schedule
    • Fix recurring failure modes and process bottlenecks
    • Update implementation guidance based on operational learning

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.