Optimizing Infrastructure and Enhancing Scalability
Transforming a cloud-based informatics platform for research scientists through AWS migration and automation.
About
The Client
The customer owns a cloud-based informatics platform built to help research scientists efficiently manage, evaluate, and share data. The platform has advanced visualization tools that allow scientists to delve deeper into the data. Considering the critical nature of the data, flawless performance is a key imperative, mandating comprehensive testing of the platform.
The
Challenge
Amazon S3 and EFS (Replacing RAID)
The existing RAID storage system introduced complexity, potential for human error, and challenges in scaling, complicating management and increasing data risks in the cloud.
RDS Aurora (Replacing MySQL)
The local MySQL database struggled with scaling, performance, and fault tolerance, requiring intensive management and leading to inefficient resource scaling.
AWS Lambda and Batch Jobs (Replacing Fixed Compute)
Manual provisioning resulted in inefficient resource utilization, increased costs, and limited scalability, hindering the platform's performance.
What Forgeahead Did
Forgeahead implemented a comprehensive migration strategy utilizing AWS services to address the client’s infrastructure challenges
Migrated to Amazon S3
Transitioning to Amazon S3 provided 99.99% durability, automated backups, fault tolerance, and cost-effective, pay-as-you-go scalability. AWS EFS enabled shared data I/O among distributed nodes, enhancing data accessibility.
RDS
Aurora
Implementing RDS Aurora offered automated scaling, high availability, multi-AZ replication, and up to 5 times faster performance than MySQL, all while minimizing operational overhead.
AWS Lambda
and Batch Jobs
Implementing AWS Lambda and Batch Jobs enabled on-demand scaling, optimizing resource usage and significantly reducing costs by using resources only when triggered. This approach allowed for automatic scaling to efficiently handle variable workloads.
Role of AWS in Building
the SaaS Application
AWS played a critical role in transforming the client’s platform by providing reliable and scalable infrastructure solutions
Data Management
and Storage
Amazon S3 and EFS simplified storage management while ensuring high availability and automated backups.
Database
Optimization
RDS Aurora enhanced performance and availability, reducing management overhead and downtime.
Scalable Computing
Resources
AWS Lambda and Batch Jobs allowed for dynamic resource allocation based on demand, improving efficiency and reducing operational costs.
Ensured automated fault tolerance and high data durability, reducing the risk of data loss and downtime.
Provided scalable, pay-as-you-go configurations, streamlining compliance without manual intervention.
Achieved improved availability, performance, and scaling capabilities with minimal management effort.
Automated backups and centralized monitoring enhanced compliance and reduced downtime significantly.
On-demand scaling optimized resource usage and lowered costs while simplifying infrastructure management.
Enabled faster task completion and reduced the need for manual interventions in batch-processing tasks.
AWS Services Used
EC2
S3
EFS
EventBridge
Lambda
Batch
SSM
Control Tower
CodeBuild
Cloud Formation
RDS Cluster
R53
DevOps Stack
Technology Stack
What’s next?
Developing a Secure, Multi-Tenant SaaS Platform for Modern Learning
