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LSREL12-BP03 Maintain data consistency in distributed research systems - Life Sciences Lens
Implementation guidance

LSREL12-BP03 Maintain data consistency in distributed research systems

Distributed life sciences workloads (for example, spanning multiple sites, cloud Regions, or CRO integrations) require mechanisms to maintain data consistency during partial failures and recovery. This includes distributed transactions, compensating actions, and reconciliation processes to improve accuracy and completeness across system components.

Desired outcome:

  • Data remains accurate and consistent across distributed components after recovery.

  • Conflicts or anomalies are detected and resolved automatically where possible.

  • Reconciliation evidence is preserved for audit and reproducibility.

Common anti-patterns:

  • No reconciliation of data between distributed systems after recovery.

  • Assuming eventual consistency will resolve discrepancies without validation.

  • Ignoring data mismatches introduced during failover or partial recovery.

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

Implementation guidance

For distributed systems, design recovery processes that reconcile states across components. This may involve compensating transactions, replaying messages, or performing checksum-based reconciliation. Where eventual consistency is used, implement monitoring and exception handling to identify unreconciled discrepancies. Document reconciliation processes in DR runbooks.

Implementation steps

  1. Use Amazon DynamoDB global tables or Amazon Aurora Global Database to maintain multi-region consistency.

  2. For asynchronous pipelines, implement reconciliation jobs using AWS Step Functions and AWS Lambda to compare datasets across Regions.

  3. Capture anomalies in Amazon CloudWatch Logs and route issues into incident workflows through Amazon EventBridge.

  4. Retain reconciliation evidence in Amazon S3 for compliance-related purposes.