AWS Well-Architected Framework

AWS Well-Architected Framework Summary

What is the AWS Well-Architected Framework?

• The AWS Well-Architected Framework is a structured approach to help architects, developers, and cloud professionals build secure, high-performing, resilient, and efficient infrastructure on Amazon Web Services (AWS).
• It helps you understand the pros and cons of decisions you make while building systems on AWS.
• It provides a way for you to consistently measure your architectures against best practices and identify areas for improvement.
• The AWS Well-Architected Tool (AWS WA Tool) provides recommendations for making your workloads more reliable, secure, efficient, and cost-effective.

Why Use the AWS Well-Architected Framework?

• Using the framework helps you learn architectural best practices for designing and operating secure, reliable, efficient, cost-effective, and sustainable workloads in the AWS Cloud.
• Having well-architected systems greatly increases the likelihood of business success.

Six Pillars of the AWS Well-Architected Framework

The AWS Well-Architected Framework is based on six pillars:

1. Operational Excellence: Focuses on running and monitoring systems, and continually improving processes and procedures.
   • Key functions include automating changes, responding to events, and defining standards to manage daily operations.
   • Design principles:
     • Perform operations as code.
     • Make frequent, small, reversible changes.
     • Refine operations procedures frequently.
     • Anticipate failure.
2. Security: Focuses on protecting data, systems, and assets.
   • Design principles:
     • Implement a strong identity foundation.
     • Enable traceability.
     • Apply security at all layers.
     • Automate security best practices.
     • Protect data in transit and at rest.
     • Keep people away from data.
     • Prepare for security events.
3. Reliability: Encompasses the ability of a workload to perform its intended function correctly and consistently when it’s expected to.
   • Design principles:
     • Automatically recover from failure.
     • Test recovery procedures.
     • Scale horizontally to increase aggregate workload availability.
     • Stop guessing capacity.
     • Manage change in automation.
4. Performance Efficiency: Focuses on using computing resources efficiently to meet system requirements, and to maintain that efficiency as demand changes and technologies evolve.
   • Design principles:
     • Democratize advanced technologies.
     • Go global in minutes.
     • Use serverless architectures.
     • Experiment more often.
     • Consider mechanical sympathy.
5. Cost Optimization: Involves running systems to deliver business value at the lowest price point.
   • Design principles:
     • Implement cloud financial management.
     • Adopt a consumption model.
     • Measure overall efficiency.
     • Stop spending money on undifferentiated heavy lifting.
     • Analyse and attribute expenditure.
6. Sustainability: Addresses the long-term environmental, economic, and societal impact of your business activities.
   • Design principles:
     • Understand your impact.
     • Establish sustainability goals.
     • Maximize utilization.
     • Anticipate and adopt new, more efficient hardware and software offerings.
     • Use managed services.
     • Reduce the downstream impact of your cloud workloads.

AWS Design Principles

The sources also outline some general AWS design principles:

• Scalability: Increasing resources to meet demand.
  • Scaling horizontally: Increasing the number of resources.
  • Scaling vertically: Increasing the specifications of an individual resource.
• Disposable Resources Instead of Fixed Servers: Automating the setup of new resources along with their configuration and code.
  • Infrastructure as Code: Applying techniques, practices, and tools from software development to make your whole infrastructure reusable, maintainable, extensible, and testable.
• Automation: AWS handles the details of resource management, such as resource provisioning, load balancing, auto-scaling, and monitoring, allowing you to focus on resource deployment.
  • Serverless Management and Deployment: Shifting your focus to automation of your code deployment, with AWS handling management tasks.
  • Alarms and Events: Continuous monitoring of resources, initiating events when certain metrics or conditions are met.
• Loose Coupling: Reducing interdependencies in a system by allowing components to interact through specific interfaces.
  • Well-Defined Interfaces: Using interfaces such as RESTful APIs to reduce interdependencies.
  • Service Discovery: Allowing applications deployed as smaller services to be consumed without prior knowledge of their network topology details.
  • Asynchronous Integration: Using intermediate durable storage for interacting components that do not need an immediate response.
  • Distributed Systems Best Practices: Building applications that handle component failure gracefully.
• Services, Not Servers: Using managed services and serverless architectures to reduce operational complexity.
  • Managed Services: Providing building blocks for developers, such as databases, machine learning, analytics, queuing, search, email, and notifications.
  • Serverless Architectures: Enabling the building of both event-driven and synchronous services without managing server infrastructure.
• Databases: Choosing the right database technology for each workload, including relational databases, NoSQL databases, data warehouses, and graph databases.
  • Search Functionalities: Enabling querying of datasets that are not precisely structured, with features like customizable result ranking, faceting for filtering, synonyms, and stemming.
• Managing Increasing Volumes of Data: Using a data lake approach to store massive amounts of data in a central location.
• Removing Single Points of Failure: Using redundancy, failure detection, and durable data storage to ensure high availability.
  • Introducing Redundancy: Using standby redundancy and active redundancy to ensure availability.
  • Detect Failure: Using health checks and log collection.
  • Durable Data Storage: Using synchronous replication, asynchronous replication, and quorum-based replication.
  • Automated Multi-Data Centre Resilience: Utilizing AWS Regions and Availability Zones (Multi-AZ Principle).
  • Fault Isolation and Traditional Horizontal Scaling: Using shuffle sharding.
• Optimize for Cost: Using right-sizing, elasticity, and various purchasing options to minimize costs.
• Caching: Using application data caching and edge caching to improve performance and reduce costs.
• Security: Using AWS features for defence in depth, sharing security responsibility with AWS, reducing privileged access, implementing security as code, and using real-time auditing.

Additional Best Practices

The sources highlight these additional best practices for building applications in the AWS cloud:

1. Decouple your components (loose coupling): Build components that do not have tight dependencies on each other so that if one component fails, the others will continue to work.
2. Think parallel: Implement parallelization whenever possible and automate processes.
3. Implement elasticity: Automate the deployment process and streamline configuration and build processes to allow the system to scale in and out to meet demand without human intervention.
4. Design for failure: Assume that components will fail and design your architecture for high availability.

Scalability and Elasticity

• Cloud Elasticity: The ability of a cloud to automatically expand or compress infrastructural resources based on demand.
• Cloud Scalability: Handling a growing workload where good performance is needed.

High Availability and Fault Tolerance

• High Availability: A system that almost always maintains uptime, though sometimes in a degraded state.
• Fault Tolerance: A system that almost always maintains uptime with no noticeable difference to users during an outage.