LSPERF02-BP03 Elastic data processing pipelines

Design data processing architectures that automatically scale to accommodate both predictable and unexpected processing demands. Implement event-driven pipelines that efficiently handle high-velocity instrument data streams while maintaining strict controls over clinical data flows, providing consistent performance during peak research periods without compromising security or regulatory requirements.

Desired outcome: Implement an adaptive data processing architecture that automatically scales to meet varying workload demands while maintaining strict security and audit controls for each data type, particularly clinical information.

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

Implementation guidance

To effectively manage high-velocity data processing, implement event-driven architectures that respond to data events in real-time, enabling efficient handling of instrument data streams. Maintain separate processing flows for different data types, applying appropriate controls to clinical data while optimizing research workloads.

Use managed AWS services like AWS Lambda, Amazon SQS, and Amazon Kinesis that automatically scale to match processing needs without manual intervention. Implement throttling mechanisms to protect downstream systems from being overwhelmed during peak research periods. Set up comprehensive monitoring to track performance and automatically adjust resources based on observed patterns, while maintaining regulatory guardrails to verify that automatic scaling doesn't compromise security requirements.

Regularly conduct load testing to verify that your architecture can scale as expected under stress, validating the system's ability to handle varying data volumes while maintaining performance and regulatory standards.

Implementation steps

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