Cloud-Native ETL Transformation in Healthcare: Performance Optimization and Cost Reduction Through AWS Infrastructure Modernization
DOI:
https://doi.org/10.22399/ijcesen.4514Keywords:
Cloud Computing, Healthcare Data Infrastructure, Etl Modernization, Gitops Automation, Aws ContainerizationAbstract
Healthcare enterprises are under constant pressure to deliver faster analytics on growing clinical and claims datasets while staying compliant and cost-efficient. This paper presents a cloud-native ETL modernization that I led for a regulated U.S. healthcare organization, migrating a legacy on-prem batch ETL stack into AWS using containerization and GitOps automation. The modern platform runs Informatica workloads inside Docker containers on Amazon ECS, orchestrated with AWS Step Functions, and lands curated outputs in Snowflake. All infrastructure and runtime configuration are defined as code using AWS CDK and version-controlled in Git with promotion pipelines in GitHub Actions.The program replaced a tightly coupled, manually operated batch system that required ~34 hours per major run and incurred fixed licensing/compute costs, with an elastic, auditable platform completing equivalent runs in ~2.5 hours (≈90% faster) and reducing per-run cost from roughly $183 to $18 (≈85% lower). Performance gains were validated via Step Functions execution histories, ECS/CloudWatch metrics, and Snowflake query logs; cost reductions were verified through AWS Cost Explorer and Snowflake billing usage. Beyond speed and cost, the modernization introduced repeatable deployment patterns, safer multi-environment promotions, and measurable improvements in reliability and compliance traceability. The resulting approach offers a practical, evidence-backed blueprint for healthcare data organizations modernizing ETL under strict regulatory constraints.
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