A comprehensive hands-on workshop demonstrating database modernization through three progressive phases, showcasing AI-assisted development with Amazon Q Developer throughout the migration journey.
Transform a legacy .NET loan application through a 3-phase modernization journey:
Phase 1: SQL Server → AWS RDS (Lift & Shift)
Phase 2: RDS SQL Server → Aurora PostgreSQL (Engine Modernization)
Phase 3: PostgreSQL + DynamoDB (Hybrid Architecture - Logs to NoSQL)
- Up to 98% cost reduction for high-volume logging workloads (DynamoDB vs PostgreSQL)
- Up to 70% performance improvement for time-series log queries with optimized NoSQL design
- Zero data loss throughout all migration phases
- Production-ready hybrid cloud architecture patterns
- Progressive Migration Strategies for risk mitigation
- AI-Assisted Development with Amazon Q Developer
- Schema Conversion from T-SQL to PostgreSQL
- Stored Procedure Refactoring to application logic
- NoSQL Integration with DynamoDB for operational data
- Hybrid Architecture patterns for modern applications
- AWS account with administrative access
- Visual Studio 2022 or VS Code with Amazon Q Developer extension
- .NET 9 SDK
- Basic knowledge of SQL Server and .NET development
Legacy On-Premises → Phase 1: AWS RDS → Phase 2: PostgreSQL → Phase 3: Hybrid Cloud
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ .NET App │ │ .NET App │ │ .NET App │ │ .NET App │
│ SQL Server │ │ RDS SQL Server │ │ Aurora PostgreSQL│ │ PostgreSQL + │
│ (On-Premises) │ │ (Managed) │ │ (Open Source) │ │ DynamoDB │
└─────────────────┘ └─────────────────┘ └─────────────────┘ └─────────────────┘
AIDev1/
├── LoanApplication/ # .NET 9 baseline application
│ ├── Controllers/ # API controllers
│ ├── Models/ # Entity models
│ ├── Data/ # Entity Framework context
│ └── Scripts/ # Database deployment scripts
├── migration/ # Migration procedures and scripts
│ ├── phase1/ # RDS SQL Server migration
│ ├── phase2/ # PostgreSQL conversion
│ └── phase3/ # DynamoDB integration
├── workshop/ # Workshop materials
│ ├── introduction.md # Workshop overview and objectives
│ ├── setup-instructions.md # Environment setup guide
│ ├── lab-instructions.md # Hands-on lab procedures
│ ├── troubleshooting-guide.md # Issue resolution with Q Developer
│ └── cleanup-procedures.md # Resource cleanup automation
├── quality-assurance/ # Testing and validation framework
└── q-developer-integration.md # AI-assisted development guide
| Phase | Duration | Objective | Key Technologies |
|---|---|---|---|
| Setup | 30 min | Environment preparation | AWS CLI, Q Developer, .NET 9 |
| Phase 1 | 90 min | Lift-and-shift to RDS | CloudFormation, RDS, S3 |
| Phase 2 | 120 min | PostgreSQL modernization | Aurora, DMS, Entity Framework |
| Phase 3 | 90 min | DynamoDB integration | DynamoDB, AWS SDK, Hybrid patterns |
| Wrap-up | 45 min | Results review and Q&A | Performance analysis, cost optimization |
This workshop showcases AI-assisted development throughout all phases:
Each phase includes guided prompts that help you:
- Understand current state and requirements
- Analyze migration complexity and risks
- Develop optimal migration strategies
- Generate production-ready code and scripts
@q Analyze this SQL Server stored procedure and recommend PostgreSQL conversion strategy
@q Design optimal DynamoDB table structure for these high-volume log access patterns
@q Generate CloudFormation template for Aurora PostgreSQL with financial services optimization
@q Create comprehensive data migration script with error handling and progress tracking
git clone https://github.com/yourusername/aws-database-modernization-workshop.git
cd aws-database-modernization-workshop# Deploy baseline application
cd LoanApplication
dotnet run
# Verify Q Developer integration
# Open Visual Studio/VS Code and test Q Developer connectivity- Setup Instructions:
workshop/setup-instructions.md - Lab Procedures:
workshop/lab-instructions.md - Troubleshooting:
workshop/troubleshooting-guide.md
- .NET 9 Web API with Entity Framework Core
- SQL Server Database with 200,000+ loan records
- 4 Stored Procedures (3 simple + 1 complex with 200+ lines)
- Financial Services Logic including DSR calculations and credit checks
- AWS RDS SQL Server (Phase 1)
- Aurora PostgreSQL (Phase 2)
- DynamoDB for high-volume logs (Phase 3)
| Metric | Baseline | Phase 1 | Phase 2 | Phase 3 |
|---|---|---|---|---|
| Infrastructure Cost | $500 | $1,000 | $800 | $650 |
| Query Response | 150ms | 145ms | 120ms | 120ms (business) / 45ms (logs) |
| Scalability | Limited | Enhanced | Optimized | Hybrid Optimized |
| Cost Component | Baseline | Phase 1 | Phase 2 | Phase 3 |
|---|---|---|---|---|
| Infrastructure | $500 | $1,000 | $800 | $650 |
| DBA/Operations | $2,000 | $500 | $400 | $350 |
| Licensing | $300 | $0 | $0 | $0 |
| Backup/DR | $200 | $0 | $0 | $0 |
| Monitoring | $100 | $0 | $0 | $0 |
| Monthly TCO | $3,100 | $1,500 | $1,200 | $1,000 |
| Annual Savings | Baseline | $19,200 | $22,800 | $25,200 |
TCO includes infrastructure, operational overhead, licensing, backup/DR, and monitoring costs
Note: These are typical benchmark figures for demonstration purposes, not actual costs
- CloudFormation Templates for infrastructure automation
- PowerShell Scripts for migration automation
- Comprehensive Validation with data integrity checks
- Performance Benchmarking and cost analysis
- Emergency Rollback procedures
- Discovery-Based Learning with 50+ Q Developer prompts
- Progressive Complexity from simple lift-and-shift to hybrid architecture
- Real-World Scenarios based on financial services requirements
- Comprehensive Documentation with troubleshooting guides
- Dual-Write Pattern for safe migration
- Service Layer Abstraction for seamless integration
- Batch Migration Tools with resume capability
- Real-Time Monitoring dashboard
- Cost Optimization demonstrating significant reduction potential
- Q Developer Integration Guide: Comprehensive AI-assisted development patterns
- Migration Best Practices: AWS database modernization strategies
- Troubleshooting Guide: Common issues and Q Developer solutions
- Performance Optimization: Cloud-native database tuning techniques
This workshop is designed for educational purposes and demonstrates production-ready migration patterns. Contributions welcome for:
- Additional migration scenarios
- Enhanced Q Developer prompts
- Performance optimizations
- Documentation improvements
This project is licensed under the MIT License - see the LICENSE file for details.
Upon completion, participants will have:
- ✅ Migrated a complete application through 3 database platforms
- ✅ Mastered AI-assisted development with Amazon Q Developer
- ✅ Implemented hybrid cloud architecture with demonstrated cost optimization patterns
- ✅ Applied AWS best practices for database modernization
- ✅ Gained hands-on experience with real-world migration challenges
Ready to modernize your databases? Start with the Workshop Introduction and transform your legacy applications into modern, cloud-native architectures with the power of AI-assisted development.
