Solutions Architect's Handbook - Third Edition: Kick-start your career with architecture design principles, strategies, and generative AI techniques

Cover
Copyright
Forewords
Contributors
Table of Contents
Preface
Chapter 1: Solutions Architect in an Organization
	What is solution architecture?
		The benefits of solution architecture
	he solutions architect’s role
		Generalist solutions architect roles
			Enterprise solutions architect
			Application Architect
			Cloud Architect
			Architect Evangelist
		Specialist solutions architect roles
			Infrastructure Architect
			Network Architect
			Data Architect
			ML Architect
			GenAI architect
			Security Architect
			DevOps architect
			Industry Architect
	Understanding a solutions architect’s responsibilities
		Analyze functional requirements (FRs)
		Define NFRs
		Understand and engage stakeholders
		Understand architecture constraints
		Make technology selections
		Develop a POC and prototype
		Solution design and delivery
			Ensuring post-launch operability and maintenance
		Solution scaling and technology evangelism
	Solutions architect in an Agile organization
	Common challenges in the solutions architect role
	Career path and skill development for solutions architects
	Summary
Chapter 2: Principles of Solution Architecture Design
	Building scalable architecture design
		Scaling static content
			Session management for application server scaling
			Database scaling
			Building elastic architecture
	Building a highly available and resilient architecture
		Highly available architecture
		Resilient architecture
		Achieving redundancy
		Addressing component failure
	Making your architecture fault-tolerant
	Designing for performance
	Creating immutable architecture
	Think loose coupling
	Think service, not server
	Think data-driven design
	Adding security everywhere
	Making applications usable and accessible
		Achieving usability
		Achieving accessibility
	Building future-proof extendable and reusable architecture
	Ensuring architectural interoperability and portability
		Making applications interoperable
			Making applications portable
	Applying automation everywhere
	Plan for business continuity
	Design for operation
	Overcoming architectural constraints
		Taking the MVP approach
	Summary
Chapter 3: Cloud Migration and Cloud Architecture Design
	Public, private, and hybrid clouds
	Solution architecture in the public cloud
		The public cloud architecture
		Popular public cloud providers
		Cloud-native architecture
		Designing cloud-native architecture
	Creating a cloud migration strategy
		Lift and shift migration
			Rehost
			Replatform
			Relocate
		The cloud-native approach
			Refactor
			Repurchase
		Retain or retire
			Retain
			Retire
	Choosing a cloud migration strategy
	Steps for cloud migration
		Discovering your portfolio and workloads
		Analyzing the information
		Creating a migration plan
		Designing the application
		Executing application migration to the cloud
			Data migration
			Server migration
		Integrating, validating, and cutover
			Validation
			Integration
			The cutover process
		Operating the cloud application
		Application optimization in the cloud
	Creating a hybrid cloud architecture
	Taking a multi-cloud approach
	Implementing CloudOps
	CloudOps pillars
	Summary
	Further reading
Chapter 4: Solution Architecture Design Patterns
	Building an n-tier layered architecture
		The web layer
		The application layer
		The database layer
	Creating a multi-tenant SaaS-based architecture
	Understanding service-oriented architecture
		RESTful web service architecture
		Building a RESTful-architecture-based e-commerce website
	Building a cache-based architecture
		Cache distribution pattern in a three-tier web architecture
		Rename distribution pattern
		Cache proxy pattern
		Rewrite proxy pattern
		App caching pattern
		Memcached versus Redis
	Model-View-Controller (MVC) architecture
		Applying MVC to design an online bookstore
	Building Domain-Driven Design (DDD)
	Understanding the circuit breaker pattern
	Implementing the bulkhead pattern
	Creating a floating IP pattern
	Deploying an application with a container
		The benefit of containers
		Container deployment
		Building container-based architecture
	Database handling in application architecture
		High-availability database pattern
	Clean Architecture
	Avoiding anti-patterns in solution architecture
	Summary
Chapter 5: Cloud-Native Architecture Design Patterns
	What is cloud-native architecture?
	Building serverless architecture
		Considerations for serverless architecture
	Building stateless and stateful architectural designs
		Stateful architecture
		Stateless architecture
	Creating a microservice architecture
		Saga pattern
		Fan-out/fan-in pattern
		Service mesh pattern
	Reactive architecture
	Building queue-based architecture
		Queuing chain pattern
		Job observer pattern
	Pipes-and-Filters Architecture
	Creating Event-Driven Architecture
		Publisher/subscriber model
		Event stream model
	Backend for Frontend pattern
	Cloud-native architecture anti-patterns
		Single point of failure
		Manual scaling
		Tightly coupled services
		Ignoring security best practices
		Not monitoring or logging
		Ignoring network latency
		Lack of testing
		Over-optimization
		Not considering costs
	Summary
Chapter 6: Performance Considerations
	Design principles for high-performance architecture
		Reducing latency
		Improving throughput
		Handling concurrency
		Applying caching
	Technology selection for performance optimization
		Making a computational choice
			Working with containers
			Going serverless
		Making a storage choice
			Working with block storage and storage area network
			Working with file storage and network area storage
			Working with object storage and cloud data storage
			Storage for databases
		Making a database choice
			Online transactional processing
			Nonrelational databases
			Online analytical processing
			Building a data search functionality
		Improving network performance
			Using edge computing
			Defining a DNS routing strategy
			Applying a load balancer
			Applying auto-scaling
	Performance considerations for mobile applications
		Optimization of load times
		Efficient use of resources
		Responsive user interface (UI)
		Network efficiency
		Battery consumption
		Cross-platform compatibility
		User experience (UX) design
		Effective data management
		Testing and quality assurance
	Performance testing
		Types of performance testing
	Managing performance monitoring
	Summary
Chapter 7: Security Considerations
Chapter 8:Architectural Reliability Considerations
	Design principles for architectural reliability
		Making systems self-healing by applying automation
			Quality assurance
		Creating a distributed system
		Monitoring and adding capacity
		Performing recovery validation
	Technology selection for architectural reliability
		Planning the RPO and RTO
		Replicating data
			Synchronous versus asynchronous replication
			Replication methods
		Planning disaster recovery
			Backup and restore
			Pilot light
			Warm standby
			Multi-site
		Applying best practices for DR
	Improving reliability with the cloud
	Summary
Chapter 9:Operational Excellence Considerations
	Design principles for operational excellence
		Automating manual tasks
		Making incremental and reversible changes
		Predicting failures and responding
		Learning from mistakes and refining
		Keeping the operational runbook updated
	Selecting technologies for operational excellence
		Planning for operational excellence
		IT asset management
			Configuration management
		The functioning of operational excellence
			Monitoring system health
		Improving operational excellence
			IT operations analytics
			Root Cause Analysis
			Auditing and reporting
	Achieving operational excellence in the public cloud
	Driving efficiency with CloudOps
	Summary
Chapter 10:Cost Considerations
	Design principles for cost optimization
		Calculating the total cost of ownership
		Planning the budget and forecast
		Managing demand and service catalogs
		Keeping track of expenditure
		Continuous cost optimization
	Understanding techniques for cost optimization
		Reducing architectural complexity
		Increasing IT efficiency
		Applying standardization and governance
			Resource cost tagging
		Monitoring cost usage and reports
	Driving cost optimization in the public cloud
	Green IT and its influence on cost considerations
		Cost-effective and green application hosting on AWS
	Summary
Chapter 11:DevOps and Solution Architecture Framework
	Introducing DevOps
		Understanding the benefits of DevOps
	Understanding the components of DevOps
		Continuous integration/Continuous deployment
		Continuous monitoring and improvement
		Infrastructure as code
		Configuration management
	Introducing DevSecOps for Security
	Combining DevSecOps and CI/CD
	Implementing a CD strategy
		In-place deployment
		Rolling deployment
		Blue-green deployment
		Red-black deployment
		Immutable deployment
	Best practices for choosing the right deployment strategy
	Implementing continuous testing in the CI/CD pipeline
		A/B testing
	Using DevOps tools for CI/CD
		Code editor
		Source code management
		CI server
		Code deployment
		Code pipeline
	Implementing DevOps best practices
	Building DevOps and DevSecOps in the cloud
	Summary
Chapter 12:Data Engineering for Solution Architecture
	What is big data architecture?
	Designing big data processing pipelines
	Data ingestion, storage, processing, and analytics
		Data ingestion
			Technology choices for data ingestion
			Ingesting data to the cloud
		Storing data
			Technology choices for data storage
			Structured data stores
			NoSQL databases
			Search data stores
			Unstructured data stores
			Object storage
			Vector Database (VectorDB)
			Blockchain data stores
			Streaming data stores
	Data storage in the cloud
		Processing data and performing analytics
			Technology choices for data processing and analysis
			Data processing in the cloud
	Visualizing data
		Technology choices for data visualization
	Designing big data architectures
		Data lake architecture
		Lakehouse architecture
		Data mesh architecture
		Streaming data architecture
		Choosing the right big data architecture
	Big data architecture best practices
	Summary
Chapter 13:Machine Learning Architecture
	What is machine learning?
		Types of machine learning
			Supervised learning
			Unsupervised learning
			Semi-supervised learning
			Reinforcement learning
			Self-supervised learning
			Multi-instance learning
	Working with data science and machine learning
		Evaluating ML models—overfitting versus underfitting
		Popular machine learning algorithms
			Linear regression
			Logistic regression
			Decision trees
			Random forests
			K-Nearest Neighbours (k-NNs)
			Support vector machines (SVMs)
			Neural networks
			K-means clustering
			XGBoost
		Popular machine learning tools and frameworks
	Machine learning in the cloud
	Building machine learning architecture
		Prepare and label
		Select and build
		Train and tune
		Deploy and manage
		ML reference architecture
	Design principles for machine learning architecture
		Organizing the machine learning system into modules
		Ensuring scalability
		Ensuring reproducibility
		Implementing data quality assurance
		Ensuring flexibility
		Ensuring robustness and reliability
		Ensuring privacy and security
		Ensuring efficiency
		Ensuring interpretability
		Implementing real-time capability
		Ensuring fault tolerance
	MLOps
		MLOps principles
		MLOps best practices
	Deep learning
		Deep learning in the real world
			Healthcare: diagnosis and prognosis
			Autonomous vehicles: navigation and safety
			Manufacturing: quality control and predictive maintenance
	NLP
		Chatbots and virtual assistants
		Sentiment analysis
		Text summarization
		Machine translation
	Summary
Chapter 14:Generative AI Architecture
	What is generative AI?
	Generative AI use cases
		Customer experience transformation
		Employee productivity enhancement
		Optimizing business operations
	The basic architecture of generative AI systems
		Types of generative models
			Generative Adversarial Networks (GANs)
			Variational Autoencoders (VAEs)
			Transformer-based generative models
			Other important generative models
		Importance of hyperparameter tuning and regularization in architectures
			Hyperparameter tuning
			Regularization
	Popular generative AI FMs
	How to start with generative AI
		For end users
		For builders
		Using generative AI FMs in your applications with public cloud providers
			Choosing the right FM
		Preventing model hallucinations
	Generative AI reference architecture for building a mortgage assistant app
	Challenges in implementing generative AI
		Training stability issues
		Mode collapse
		Latent space interpolation challenges
		Ethical concerns and misuse
	Summary
Chapter 15:Rearchitecting Legacy Systems
	Learning the challenges of legacy systems
		Difficulty in keeping up with user demand
		Higher cost of maintenance and updates
		Shortage of skills and documentation
		Vulnerability to corporate security issues
		Incompatibility with other systems
	Defining a strategy for system modernization
		Assessment of a legacy application
		Defining the modernization approach
		Benefits of system modernization
	Looking at legacy system modernization techniques
		Encapsulation, rehosting, and replatforming
		Refactoring and rearchitecting
		Redesigning and replacing
	Defining a cloud migration strategy for legacy systems
		Documentation and support
	Mainframe migration with the public cloud
		Challenges of mainframe modernization
		Migrating standalone applications
		Migrating applications with shared code
			Application decoupling using a standalone API
			Application decoupling using a shared library
			Application decoupling using message queues
		Benefits of using the public cloud for mainframe modernization
	Modernizing legacy code with generative AI
	Summary
Chapter 16:Solution Architecture Document
	Purpose of the SAD
	Views of the SAD
	Structure of the SAD
		Solution overview
		Business context
		Conceptual solution overview
		Solution architecture
			Information architecture
			Application architecture
			Data architecture
			Integration architecture
			Infrastructure architecture
			Security architecture
		Solution implementation
		Solution management
		Appendix
	Life cycle of the SAD
	SAD best practices and common pitfalls
	IT procurement documentation for a solution architecture
	Summary
Chapter 17:Learning Soft Skills to Become a Better Solutions Architect
	Importance of soft skills in solution architecture
	Acquiring pre-sales skills
		Key skills
		Presenting to C-level executives
	Taking ownership and accountability
		Defining strategy execution with OKRs
		Thinking big
	Being flexible and adaptable
	Design thinking
	Being a builder by engaging in coding hands-on
	Becoming better with continuous learning
	Being a mentor to others
	Becoming a technology evangelist and thought leader
	Summary
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Index