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Architecture Builders

Enterprise-scale architecture generators that create complete system blueprints with multiple coordinated components, following best practices for scalability, maintainability, and operational excellence.

Available Architecture Builders​

Enterprise System Architectures​

Transactional Architecture Builder

Complete transactional architecture with microservices, domain gateways, federated GraphQL, and comprehensive infrastructure.

AdvancedMicroservicesGraphQLJavaRust

Domain Architecture Builder

Domain-focused architecture generator for building cohesive domain services with GraphQL gateways following DDD principles.

AdvancedDomain-Driven DesignGraphQLJava

ML Architecture Builder

Comprehensive machine learning architecture with MLOps pipelines, model serving, and monitoring for production ML systems.

AdvancedMLOpsPythonKubernetes

Architecture Philosophy​

Composable Components​

Architecture builders orchestrate multiple specialized archetypes to create cohesive systems:

  • Component Orchestration: Multiple archetypes working together
  • Consistent Integration: Seamless communication between components
  • Shared Infrastructure: Common operational concerns
  • Unified Documentation: Comprehensive system documentation

Enterprise Patterns​

All architecture builders implement enterprise-grade patterns:

  • Scalability: Horizontal and vertical scaling capabilities
  • Reliability: Fault tolerance and recovery mechanisms
  • Security: Defense in depth and zero-trust principles
  • Observability: Comprehensive monitoring and alerting

Architecture Comparison​

FeatureTransactionalDomainML Platform
Primary FocusEnterprise transactionsDomain boundariesML operations
Core TechnologiesJava, Rust, GraphQLJava, GraphQLPython, Kubernetes
Services Generated5-10 microservices2-3 domain services10+ ML components
ComplexityVery HighHighVery High
Team Size10-20 developers5-10 developers5-15 ML engineers
DeploymentMulti-clusterSingle clusterMulti-environment

System Architecture Patterns​

Transactional Architecture​

Domain Architecture​

ML Architecture​

Technology Stack Overview​

Common Technologies​

All architecture builders leverage common enterprise technologies:

  • Containerization: Docker for application packaging
  • Orchestration: Kubernetes for container management
  • Monitoring: Prometheus/Grafana for observability
  • Documentation: mdBook for architecture documentation
  • CI/CD: GitHub Actions for automation

Specialized Technologies​

Transactional Architecture​

  • Backend: Java Spring Boot, Rust Apollo Router
  • API: GraphQL federation, gRPC services
  • Database: PostgreSQL, Redis
  • Messaging: Apache Kafka, RabbitMQ

Domain Architecture​

  • Backend: Java Spring Boot
  • API: GraphQL, gRPC
  • Database: PostgreSQL, Redis
  • Patterns: DDD, CQRS, Event Sourcing

ML Architecture​

  • ML Frameworks: PyTorch, TensorFlow, Scikit-learn
  • MLOps: MLflow, Kubeflow, Ray
  • Data: Apache Spark, Delta Lake, Feast
  • Serving: FastAPI, KServe, Seldon

Getting Started Guide​

1. Choose Your Architecture​

Select based on your system requirements:

# For enterprise transactional systems
archetect render git@github.com:p6m-archetypes/transactional-architecture-builder.archetype.git

# For domain-focused applications
archetect render git@github.com:p6m-archetypes/domain-architecture-builder.archetype.git

# For ML/AI platforms
archetect render git@github.com:p6m-archetypes/ml-architecture-builder.archetype.git

2. Configuration Process​

All builders follow a similar configuration flow:

  1. Organization Setup: Company details and naming conventions
  2. Technology Selection: Database, messaging, deployment options
  3. Component Selection: Which services and features to include
  4. Environment Configuration: Development, staging, production settings

3. Development Workflow​

Common development patterns across all architectures:

# Local development setup
docker-compose up -d

# Run tests
./scripts/test-all.sh

# Deploy to staging
./scripts/deploy.sh staging

# Deploy to production
./scripts/deploy.sh production

Best Practices​

System Design​

  • Bounded Contexts: Clear service boundaries
  • Loose Coupling: Services communicate through well-defined APIs
  • High Cohesion: Related functionality grouped together
  • Separation of Concerns: Clear layer responsibilities

Development Process​

  • Infrastructure as Code: All infrastructure versioned and automated
  • Test-Driven Development: Comprehensive test coverage
  • Continuous Integration: Automated build and test pipelines
  • Documentation: Living documentation that evolves with code

Operational Excellence​

  • Monitoring: Comprehensive observability across all layers
  • Alerting: Proactive issue detection and notification
  • Disaster Recovery: Automated backup and recovery procedures
  • Security: Defense in depth with regular security audits

Team Organization​

  • DevOps Culture: Shared responsibility for operations
  • Cross-functional Teams: Teams own services end-to-end
  • Documentation Standards: Consistent documentation practices
  • Knowledge Sharing: Regular architecture reviews and discussions

Migration Strategies​

From Monolith to Microservices​

  1. Strangler Fig Pattern: Gradually replace monolith components
  2. Database Decomposition: Split shared databases by domain
  3. API Gateway Introduction: Implement centralized routing
  4. Event-Driven Architecture: Decouple through events

Adding ML Capabilities​

  1. Data Pipeline First: Establish data infrastructure
  2. Experiment Platform: Set up MLflow and experiment tracking
  3. Model Serving: Implement serving infrastructure
  4. Monitoring Integration: Add ML-specific monitoring

Domain Refinement​

  1. Bounded Context Analysis: Identify natural domain boundaries
  2. Event Storming: Map domain events and aggregates
  3. Service Extraction: Extract cohesive domain services
  4. API Design: Create domain-specific APIs

Success Metrics​

Technical Metrics​

  • Deployment Frequency: How often you deploy
  • Lead Time: Time from commit to production
  • Mean Time to Recovery: Time to recover from incidents
  • Change Failure Rate: Percentage of deployments causing issues

Business Metrics​

  • Feature Delivery Speed: Time to deliver new features
  • System Reliability: Uptime and error rates
  • Developer Productivity: Lines of code, story points, cycle time
  • Customer Satisfaction: User experience metrics

Quality Metrics​

  • Test Coverage: Percentage of code covered by tests
  • Technical Debt: Amount of legacy code requiring refactoring
  • Security Vulnerabilities: Number and severity of security issues
  • Performance: Response times and throughput metrics

Support & Community​

Documentation Resources​

  • Architecture Decision Records (ADRs) - Documented within each generated project
  • Deployment Guides - Included in generated project documentation
  • Troubleshooting - Available in project-specific guides
  • Best Practices - Embedded in archetype templates

Community Support​

  • GitHub Discussions for architecture questions
  • Regular office hours with architecture team
  • Slack channels for real-time support
  • Monthly architecture review sessions

Training & Certification​

  • Architecture patterns workshops
  • Technology-specific training tracks
  • Certification programs for key technologies
  • Conference presentations and case studies