70%
Reduction in ETL Runtime
99.9%
Uptime Achieved
2 Hours
vs 6 Hours Before
40%
Dev Productivity Boost
Business Challenge
The client faced several critical problems:
- Fragmented data silos across EHR systems, lab systems, and insurance billing platforms
- Legacy ETL jobs that were slow, error-prone, and not scalable
- Lack of real-time data pipelines for operational dashboards
- Increasing compliance scrutiny due to Protected Health Information (PHI) handling
- Infrastructure that was manual, non-scalable, and difficult to audit
Performance Improvements
ETL Processing Time
System Performance Metrics
- Before
- After
Developer Productivity Over 9 Months
- Productivity %
Our Solution
1Data Engineering Solution
- Implemented secure ingestion pipelines using Apache Kafka and AWS Kinesis for streaming
- Built scalable ETL workflows using Apache Airflow and Spark on EMR
- Designed a HIPAA-compliant data lakehouse on AWS S3 + Delta Lake
- Automated data de-identification & tokenization for PHI
- Integrated real-time dashboards (Power BI + Redshift)
2DevOps & Infrastructure
- Adopted Infrastructure as Code (IaC) with Terraform and AWS CloudFormation
- Set up CI/CD pipelines (GitHub Actions + ArgoCD)
- Deployed Kubernetes (EKS) for containerized data services
- Established observability stack with Prometheus + Grafana + ELK
- Designed disaster recovery & backup policies with automated testing
3HIPAA Compliance
- Implemented automated compliance checks
- Worked with client's compliance team for third-party audits
- Ensured encryption at rest and in transit (TLS 1.2+)
- Row-level access controls and PHI protection
Architecture & Infrastructure
Visual representation of our healthcare data engineering and DevOps architecture
EHR Integration Architecture
Secure data lakehouse with HIPAA-compliant encryption and access controls
Data Science Workflow
Apache Airflow orchestration with Spark processing on AWS EMR
CI/CD Pipeline
Automated deployments with GitHub Actions and ArgoCD on Kubernetes
Outcomes & Impact
70% reduction in ETL pipeline runtime (from 6 hours to under 2 hours)
99.9% uptime achieved for critical data services
Near real-time dashboards improved decision-making
Successfully passed HIPAA compliance audit with zero major findings
Improved developer productivity by 40% through CI/CD
Scalable foundation for ML-driven patient risk stratification
Key Takeaways
A modern data engineering + DevOps approach in healthcare requires security-first design. Embedding compliance into infrastructure and pipelines from day one avoids costly rework.
Strong observability and automation enable healthcare organizations to be both compliant and agile.