LSREL13-BP02 Monitor data integrity across scientific processing pipelines
Implement monitoring specifically for data integrity across research pipelines. Track errors such as checksum mismatches, validation failures, or audit trail gaps. For scientific domains like genomics or clinical processing, add plausibility checks to detect biologically impossible or outlier results that may signal workflow errors.
Desired outcome:
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Early detection of data corruption or processing errors.
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Continuous assurance of data accuracy, completeness, and traceability.
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Adherence to data integrity expectations for regulated research.
Common anti-patterns:
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Only validating data at ingestion or final outputs, not during processing.
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Ignoring intermediate pipeline results when monitoring for errors.
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Not capturing or preserving logs for data validation failures.
Benefits of establishing this best practice:
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Protects reproducibility by keeping datasets accurate across processing steps.
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Reduces wasted compute from propagating corrupted or invalid data.
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Strengthens audit confidence through consistent integrity checks.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Incorporate integrity checks throughout pipelines. Validate the following during this process:
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File checksums
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Schema conformance
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Expected data patterns
Retain metadata and logs for traceability. Define biologically relevant plausibility thresholds (for example, variant frequencies or image metrics) to detect anomalies early. Integrate alerts with workflow orchestration so that failed steps are isolated.
Implementation steps
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Validate file integrity with Amazon S3 ETag checks or checksum verification jobs in AWS Lambda.
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Store audit trails in Amazon DynamoDB or Amazon S3.
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Use AWS Glue DataBrew or AWS Data Quality rules for schema and value checks.
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Implement plausibility validations in genomics workflows through AWS HealthOmics Workflows.
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Route alerts through Amazon EventBridge into incident response pipelines.
