WAF

• AWS Well-Architected Framework
• Follows certain design principles and best practices within 5 pillars
• Key AWS services: CloudFormation (op-as-code), CloudWatch/CloudTrail (monitor), ElasticSearch (improvement opportunities)

Operational Excellence

• Design principles
  • Perform operations as code
  • Annotate documentation
  • Make frequent, small, reversible changes
  • refine operations procedures frequently
  • Anticipate failures (pre-mortem)
  • Learn from failures
• Best practices
  • Prepare: goals: design workloads with mechanisms to monitor, validate
  • Operate: define success criteria, health measurement, establish baselines
  • Evolve: continuous, incremental improvement

Security

• Design Principles
  • strong identity foundation: principle of least privilege, separation of duty
  • traceability: monitor, alert, audit
  • secure all layers: (instead of just the outer layer), secure edge network, VPC, subnet, LB, instance, OS, Apps
  • automate: use policies and controls as code
  • keep people and data separate: no direct access to or manual processing of data
  • incident management: have a process for reporting and mitigating security incidents
• Best practices
  • IAM: Authenticate and authorize, Use AWS IAM
  • Detective controls: log, events processing and monitoring => CloudTrail, CloudWatch, S3 logs, API logs
  • Infrastructure protection: Use VPC controls for networks; Use compute resource config (EC2/ECS/Beanstalk)
  • Data protection: Use encryption and versioning
  • Incident response:

Reliability

• Ability to recover from infra, service, capacity failures
• Design Principles
  • test recovery procedures:
  • automate: monitor KPIs and trigger recovery
  • scale horizontally: HA
  • enough capacity: let cloud ensure capacity to prevent capacity related failures (eg. DOS)
  • change automation: changes should be automated
• Best practices
  • Foundations: manage service limits (from under/over provisioning) and network topology using IAM and VPC
  • Change mgt: elasticity (for auto scale), logs (for failure detection)
  • Failure mgt: have backups with MTTR (mean time to recover) and RPO (recovery point objectives)

Performance Efficiency

• using computing resources efficiently with changes in demand and technologies
• Design Principles
  • democratize advanced technologies: let cloud manage advanced technologies (eg. NoSQL DB, transcoding)
  • easy globalization: use cloud to expand easily
  • serverless architecture: services make efficient use of resources
  • experiment: try alternate resources (such as storage, computer) easily
  • mechanical sympathy: pick best technology (e.g. pick right DB platform)
• Best practices
  • Selection: computer/storage/database/network. , choose best tech for the need (e.g. pick fully managed dynamoDB for low latency)
  • Review: read AWS blogs to constantly look for better solutions
  • Monitoring: AWS CloudWatch to monitor performance
  • Tradeoffs: ElastiCache, CloudFront to increase performance. Use read-replicas in RDS

Cost Optimization

• using computing resources efficiently with changes in demand and technologies
• Design Principles
  • adopt a consumption model: (vs capacity model), pay only what you consume
  • measure efficiency: measure business output v/s cost to deliver it
  • avoid data-center ops: focus on business customers instead of IT infrastructure
  • used managed service or application: such as databases to reduce TCO
• Best practices
  • Expenditure awareness: AWS Cost Explorer to track exact spending, AWS Budget to set up notifications
  • Cost effective resources: choose right resource, e.g. CPU optimized EC2 instance might be right, use spot/reserved instances
  • Match supply and demand: use Auto Scaling, lambda
  • Optimize over time: Be informed about new AWS services, consult AWS trusted advisor