Cynchealth

Introduction

Cynchealth, a healthcare analytics company, sought to modernize its IT infrastructure and data analytics capabilities to offer real-time insights and analytics to healthcare providers. The goal was to create a scalable, efficient, and secure cloud-based solution leveraging AWS services, focusing on rapid data processing, visualization, and deployment.

Objectives

• Transition from MySQL to various AWS managed database services for better scalability and performance.
• Develop a robust API for seamless data interaction.
• Implement Apache Superset for advanced analytics visualization.
• Create efficient ETL processes for data management.

Solution Overview

1. API Development:
   1. Services Used: AWS API Gateway, Lambda, Python, AWS SDK (Boto3).
   2. Implementation: Dasnuve designed and implemented a RESTful API using AWS API Gateway which interfaced with Lambda functions written in Python. These functions handled data retrieval, processing, and API calls, ensuring scalability and low latency. Authentication was managed through Okta, ensuring secure access for different healthcare providers.
2. Database Migration and Management:
   1. Services Used: Amazon RDS for PostgreSQL, Amazon Redshift, DynamoDB.
   2. Strategy: Cynchealth's traditional MySQL setup was migrated to AWS RDS using PostgreSQL for its transactional data needs, with Redshift for analytical processing. DynamoDB was employed for fast, low-latency data retrieval for certain applications. ElastiCache was integrated for caching frequently accessed data, reducing the load on the database.
3. Data Analytics and Visualization:
   1. Services Used: Apache Superset, AWS EKS
   2. Implementation: Apache Superset was deployed on Kubernetes for scalability, managed by Helm charts and Terraform for automation. This setup allowed Cynchealth to visualize complex health analytics data from multiple sources directly through Superset, which was integrated with Redshift for data querying.
4. ETL Processes:
   1. Services Used: AWS ECS, AWS S3, Python for ETL scripting, AWS ECR
   2. Process: Custom ETL solutions were developed using Python scripts hosted on ECS and containerization. These scripts managed data ingestion from various healthcare providers, normalized the data, and stored it in a partitioned S3 structure, before processing into the analytics pipeline or databases. Parallel computing techniques were employed to handle high volumes of data efficiently.
5. Infrastructure as Code:
   1. Tools: Terraform, AWS CDK with Python
   2. Outcome: The entire architecture was defined using Infrastructure as Code with Terraform and AWS CDK, allowing for version-controlled infrastructure, ease of updates, and disaster recovery. This approach significantly reduced the time to market for new features and ensured consistency across environments.

Results

• Improved Scalability: The solution now scales automatically with demand, handling peak loads from multiple hospitals without performance degradation.
• Enhanced Analytics: With Apache Superset, Cynchealth's clients can now perform complex queries and visualizations, leading to better decision-making processes.
• Rapid Deployment: Using CDK and Terraform, Cynchealth can deploy new features or entire environments quickly, which was crucial for staying competitive in the fast-paced healthcare analytics market.
• Cost Efficiency: Moving to AWS services reduced infrastructure management costs and leveraged AWS's pay-as-you-go model for significant savings.

Conclusions

Dasnuve's strategic implementation of AWS technologies for Cynchealth transformed its operational capabilities, providing a robust, scalable, and secure platform for healthcare analytics. This case study exemplifies how leveraging cloud services can revolutionize traditional industries by offering innovative, scalable solutions tailored to specific needs.