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LSOPS06-BP02 Implement data pipeline testing - Life Sciences Lens
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LSOPS06-BP02 Implement data pipeline testing

Create data handling tests including user authentication and authorization, data collection, and ingestion into later warehouses. Tests should cover quality in addition to technical requirements.

Desired outcome: Data is reliable and ready for immediate business use. Reports and analytics can be trusted without manual verification.

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

Implementation guidance

Cloud technologies allow for comprehensive test environments that can replicate entire system architectures and automate complex test scenarios.

Consider implementing a testing strategy that follows data through its complete lifecycle, from initial entry through the processing stages to final storage. Cloud-based environments can replicate your production architecture, allowing validation of the integration points and data transformations. Automated testing can run scenarios that simulate real-world usage patterns, while data validation tools verify information remains consistent as it moves through different systems.

When implementing testing, focus on critical data paths that impact patient safety, product quality, or regulatory adherence. Consider continuous testing approaches that run core validation scenarios automatically when changes occur, supplemented by more comprehensive testing at key milestones.

Implementation steps

  1. Map complete data flows across each system component:

  • Use AWS X-Ray to trace data paths across distributed systems.

  • Consider AWS AWS Glue Data Catalog for documenting data structures and relationships.

  1. Create realistic test environments that replicate production architecture:

  • Implement AWS CloudFormation templates for consistent environment deployment.

  • Consider Service Catalog for standardized test environment provisioning.

  1. Develop test scenarios that follow data through complete processing paths:

  • Consider AWS Step Functions for orchestrating complex test workflows.

  1. Implement data validation checks at key integration points:

  • Use AWS Lambda functions for custom validation logic.

  • Consider Amazon EventBridge for coordinating validation across services.

  1. Automate testing as part of deployment pipelines:

  • Implement AWS CodePipeline for continuous testing integration:

  • Consider AWS CodeBuild for running test suites:

  1. Monitor data quality metrics throughout test runtime:

  • Configure Amazon CloudWatch for tracking data quality indicators.

  • Consider AWS Glue DataBrew for data quality validation.

  1. Generate comprehensive test reports for regulatory documentation:

  • Store test evidence in Amazon S3 with appropriate retention policies.

  • Consider AWS Systems Manager Automation for standardized report generation.

Resources

Related guides, videos, and documentation:

Related tools: