Geographic scalability journey of a Solar-tech company

Introduction

This case-study explores the scalability journey of a leading Solar tech company based in Bengaluru, India, who provides clean energy solutions in India and the USA.

Their infrastructure is primarily hosted in the Azure West India region, which makes it difficult to scale their operations across regions in India and the USA.

Problem

1. Inability to scale efficiently across regions in India and the USA.
2. Availability issues affecting the timely delivery of software patches to customers.
3. Database bottlenecks impacting the performance of managed databases hosted in Azure.

Objectives

1. Migrate from managed databases on Azure to self-hosted databases for improved scalability.
2. Achieve seamless scaling of infrastructure across both the India and the USA regions.
3. Address availability issues and improve the overall reliability of the services.

Implementation

1. Scaling Across Regions:
   • Adopted Azure Virtual Networks for secure and efficient communication between regions.
   • Utilized Azure Traffic Manager for intelligent load balancing and distribution of user requests.
   • Implemented auto-scaling mechanisms to adjust resource allocation based on demand.
2. Database Migration:
   • Migrated from Azure managed databases to self-hosted databases for increased control and scalability.
   • Implemented database sharding for efficient data distribution across regions.
3. CI/CD Pipeline Integration:
   • Integrated GitLab CI/CD pipelines for automated build, testing and deployment.
   • Utilized Kubernetes for consistent deployments across different environments.
   • Implemented blue-green deployment strategies for minimizing downtime during updates.

Results

1. Database Scalability Metrics:
   • Achieved 35% reduction in database service bottlenecks.
   • Self-hosted databases provided a 40% improvement in overall database performance.
2. Scaling Across Regions Metrics:
   • Seamless scaling across regions with a 60% reduction in deployment time.
   • In the India region, 99.95% uptime was achieved, and 97.8% was achieved in the USA region.
3. CI/CD Pipeline Metrics:
   • 30% reduction in release failures with automated testing.
   • 95% success rate in deployments, minimizing human errors.

Challenges

1. Database Migration Complexity:
   • Overcoming challenges associated with migrating large datasets.
   • Learning: Thorough planning and coordination with stakeholders are critical for successful database migration.
2. Cross-Region Communication:
   • Addressing latency issues and ensuring efficient communication between regions.
   • Learning: Utilizing Azure Virtual Networks effectively and optimizing network configurations for low latency.
3. Blue-Green Deployment Refinement:
   • Fine-tuning blue-green deployment strategies to minimize downtime further.
   • Learning: Continuous refinement of deployment processes based on real-world scenarios is necessary for optimal performance.

Conclusion

The successful implementation of geographic scaling on Azure cloud and DevOps practices has resulted in enhanced scalability, reduced database bottlenecks, and improved availability across regions in India and the USA. The company now operates with increased efficiency, delivering reliable solutions to a wider customer base.