AWS Solutions Architect Associate (SAA-C03)
Chapter 10: Cloud Architecture & Well-Architected Framework
INFOGRAPHIC: Architecture at a Glance
1. The 6 Pillars of Well-Architected Framework
Operational Excellence
Security
Reliability
Performance Efficiency
Cost Optimization
Sustainability
2. The 12-Factor App & AWS Mapping
3. Popular Architecture Patterns
Tiered (N-Tier)
Separation of Presentation, App, and Data layers. Classic and robust.
Microservices
Decoupled small services. Scalable and independent deployment.
Event-Driven (EDA)
Reactive systems using Producers, Consumers, and Brokers (SNS/SQS).
Serverless
No server management. FaaS (Lambda) and BaaS (Amplify/Cognito).
Study Guide: Core Concepts
Key Resilience Metrics
RTO (Recovery Time Objective)
The “Downtime” clock. How quickly must you restore service?
RPO (Recovery Point Objective)
The “Data Loss” clock. How much data (in time) can you afford to lose?
Architectural Considerations
- Elasticity: Automatically adjusting resources (Auto Scaling).
- Decoupling: Removing dependencies using queues (SQS).
- Loose Coupling: Components function independently; if one fails, others continue.
- Design for Failure: Assume everything will fail eventually. Use Multi-AZ and Redundancy.
The Well-Architected Framework: Deep Dive
1. Operational Excellence
Focus: Running and monitoring systems. Key tool: CloudFormation (Infrastructure as Code).
2. Security
Focus: Protecting data and assets. Key tools: IAM, KMS (Encryption), GuardDuty.
3. Reliability
Focus: Ability to recover from disruptions. Key tools: Route 53 (DNS Failover), Multi-AZ RDS.
4. Performance Efficiency
Focus: Using resources efficiently. Key strategy: Right-sizing and choosing the correct instance types.
5. Cost Optimization
Focus: Avoiding unnecessary costs. Key tools: Cost Explorer, Trusted Advisor, Savings Plans.
6. Sustainability
Focus: Minimizing environmental impact. Key strategy: Maximizing utilization and reducing waste.
Data-Driven Architectures
- Data Lake: Centralized repository for raw data (S3).
- Data Warehouse: Structured data optimized for complex queries (Redshift).
- Real-time Processing: Streaming data analysis (Kinesis).