Optimizing Performance and Costs for Rapidly Growing Solar Energy Platform
Successfully migrating from AWS to Azure while resolving critical database performance issues and reducing cloud costs by 36%
Overview
An innovative solar energy management platform headquartered in Chennai, providing monitoring and optimization solutions for over 750 commercial and utility-scale solar installations across India, UAE, and Southeast Asia. Their platform processes data from more than 45,000 IoT sensors, providing real-time analytics and predictive maintenance capabilities critical for maximizing energy production.
Following their parent company's strategic partnership with Microsoft, the company needed to migrate from AWS to Azure while addressing persistent database performance issues that threatened their ability to scale with rapidly growing customer installations.
Business Challenges
Database Performance Issues
Query response times exceeding 8-12 seconds during peak monitoring periods
Historical data queries timing out after 15 minutes for large installations
Performance degradation when handling data from more than 50 sensors per site
Analytical processing blocking real-time monitoring functions
Complex Migration Requirements
Tightly integrated AWS services with limited documentation
Continuous data ingestion requiring minimal downtime during migration
Custom monitoring dashboards with complex visualization requirements
Legacy codebase with AWS-specific dependencies and hardcoded references
Cost Management Concerns
Unpredictable cloud spending fluctuating by 40-50% monthly
Overprovisioned resources idle during nighttime hours
Data storage costs increasing at 2.5x the rate of new customer acquisition
Limited visibility into cost drivers and optimization opportunities
Our Solution
We implemented a comprehensive migration and optimization strategy addressing performance, cost, and business continuity concerns.
Assessment & Strategy
We conducted a thorough analysis of the existing infrastructure, application architecture, and database performance to create a holistic improvement strategy.
Database Analysis
Performed comprehensive performance profiling of database workloads
Analyzed query patterns and identified optimization opportunities
Created data access patterns map highlighting bottlenecks
Benchmarked performance against industry standards for IoT platforms
Cloud Architecture Assessment
Mapped current AWS services to Azure equivalents
Identified application components requiring redesign
Evaluated data transfer requirements and network topology
Created dependency map for migration sequencing
Cost Optimization Planning
Analyzed historical resource utilization patterns
Identified opportunities for rightsizing and automated scaling
Created TCO comparison between current and target states
Developed ROI model for migration and optimization investments
Business Impact & Results
Database Performance
•Reduced average query time from 8–12s to just 1.8s
•Historical data queries now complete in under 3 minutes
•Successfully handled 3x sensor density without performance degradation
•Eliminated timeouts during peak monitoring periods
Migration Success
•Completed migration with less than 4 hours of scheduled downtime
•Achieved 100% data integrity with no reconciliation issues
•Successfully onboarded 12 new installations during migration period
•Maintained all integration points with third-party systems
Cost Optimization
•Reduced monthly cloud spend from ₹14.6 lakhs to ₹9.3 lakhs
•Improved resource utilization from average 28% to 72%
•Decreased storage costs by 45% through appropriate tiering
•Implemented predictable spending model aligned with business growth
Business Impact
•Scaled capacity from 45K to 110K sensors with no extra resources
•Extended data retention from 18 months to 5 years cost-efficiently
•Enabled predictive maintenance features previously constrained by performance
•Accelerated customer onboarding from 15 days to 6 days
"VegaStack delivered far more than a straightforward cloud migration. They transformed our platform's performance while significantly reducing our operational costs."
Key Takeaways
Database-First Approach
Addressing database performance issues before migration prevented carrying existing problems to the new environment.
Hybrid Transition Strategy
Implementing hybrid connectivity enabled phased migration with minimal downtime.
Solar Cycle Optimization
Tailoring resources to follow solar production patterns yielded significant cost savings.
Cost-Performance Harmony
Finding the right balance between performance and cost created a sustainable platform for growth.
Conclusion
This engagement successfully migrated a complex IoT platform from AWS to Azure while resolving critical performance issues and optimizing costs. By taking a holistic approach that addressed technical, operational, and financial concerns simultaneously, we delivered a solution that exceeded the client's expectations on all fronts.
The optimized platform now provides the foundation for their continued expansion across Asia and the Middle East. With dramatically improved query performance, they can support larger installations with more sensors while providing the real-time analytics their customers depend on for maximizing solar energy production. The established cost optimization practices ensure they can scale efficiently, with cloud spending growing proportionally to revenue rather than exceeding it.
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