Data Analytics
A US-based data company approached our team with a critical business need: migrate their legacy on-premises application to AWS cloud infrastructure. The challenge was multifaceted—the application handled both transactional workloads and complex analytics, required zero downtime during migration, and needed to demonstrate clear cost benefits. This was the organization's first managed services engagement exceeding one million dollars, making success critical for future business growth.
The existing infrastructure was a monolithic application that had grown over years of development. We needed to architect a solution that not only moved the application to the cloud but fundamentally transformed how it operated—improving performance, reducing costs, and establishing a foundation for future cloud-native development.
We began with a comprehensive application assessment to understand workload patterns, data flows, and performance requirements. Our team made the strategic decision to split the application into two distinct streams: Transactional and Analytics. Each stream had different performance characteristics, scalability requirements, and cost optimization opportunities.
Key Design Principle: We applied frugal architecture principles throughout the solution, ensuring every AWS service selection was appropriate for its specific function, performant, and cost-effective. This wasn't about using the latest services—it was about using the right services.
AWS Fargate (ECS)
AWS Elasticache
AWS Aurora (PostgreSQL)
AWS Lambda
AWS S3
AWS Redshift
For transactional workloads requiring consistent performance and rapid response times, we implemented AWS Fargate on ECS for serverless container management. This eliminated the operational overhead of managing EC2 instances while providing automatic scaling based on demand. We deployed AWS Elasticache to create a high-performance caching layer, achieving sub-millisecond latency for frequently accessed data and significantly reducing database load.
The database layer uses AWS Aurora PostgreSQL, providing high availability through automated backups, point-in-time recovery, and read replicas for horizontal scalability. Aurora's architecture ensures the transactional workload maintains consistency while handling increased load.
The analytics stream required a different approach optimized for batch processing and large-scale data analysis. We implemented AWS Lambda for event-driven compute, processing analytical tasks without maintaining always-on infrastructure. Data is staged in AWS S3 before being processed and loaded into AWS Redshift for complex analytical queries and business intelligence workloads.
We configured AWS Database Migration Service (DMS) for continuous data replication from the on-premises databases to AWS. This approach enabled us to maintain data synchronization during the migration period, validate data integrity, and ultimately achieve a zero-downtime cutover when we switched production traffic to the cloud environment.
This project reinforced the importance of treating cloud migration as a transformation opportunity rather than a simple lift-and-shift. By splitting the application into workload-appropriate streams, we achieved better performance and cost optimization than a monolithic migration would have provided.
The collaboration with the client's technical team proved invaluable. Their deep understanding of the application's behavior, combined with our cloud architecture expertise, enabled us to make informed decisions about service selection and optimization strategies. This partnership approach ensured the solution met both technical requirements and business objectives.