Architecture Review Board (ARB) Submission Template
Purpose
The ARB Submission provides a structured format for proposing architectural decisions, significant technology changes, or new initiatives for formal review and approval by the Architecture Review Board.
When to Use
- Proposing new architecture patterns or frameworks
- Introducing new technologies to the technology stack
- Making significant architectural changes to existing systems
- Planning major infrastructure initiatives
- Requesting governance exemptions
- Seeking alignment on strategic architecture decisions
Submission Overview
Title: [Clear, concise title of proposal]
Submitter: [Name, Team, Date]
Status: [Draft/Ready for Review/Accepted/Rejected]
Decision: [Pending/Approved/Approved with Conditions/Rejected]
Section 1: Executive Summary
Provide a brief overview of the proposal that someone unfamiliar with your area can understand:
Business Context:
- What business problem or opportunity are we addressing?
- Why is this important now?
- What would happen if we don't proceed?
Proposed Solution:
- What are we proposing?
- Why is this the right approach?
- What are the expected outcomes?
Key Benefits:
1. Benefit 1: Expected impact (quantified if possible)
2. Benefit 2: Expected impact
3. Benefit 3: Expected impact
Key Risks:
1. Risk 1: Mitigation strategy
2. Risk 2: Mitigation strategy
3. Risk 3: Mitigation strategy
Example: Microservices Transition
Business Context:
- Current monolithic architecture is limiting feature velocity
- Team size has grown from 2 to 12 engineers
- Database queries increasingly complex (100+ ms latencies)
- Deployment cycle is 2 weeks; competitors deploy daily
Proposed Solution:
- Transition to microservices architecture
- Implement domain-driven design with 4 initial services
- Use Kubernetes for orchestration
- Implement asynchronous communication via Kafka
Key Benefits:
1. Independent deployments: Reduce deployment cycle from 2 weeks to daily
2. Team autonomy: Enable 3 independent teams to work in parallel
3. Scalability: Scale order service independently during peak demand
4. Technology diversity: Use appropriate databases (PostgreSQL, MongoDB, Elasticsearch)
Key Risks:
1. Operational complexity: Mitigate with Kubernetes automation + monitoring
2. Data consistency: Implement saga pattern + eventual consistency strategy
3. Network latency: Optimize with service mesh + caching layer
Section 2: Detailed Problem Statement
Articulate the specific problem you’re solving:
Current State:
- Description of the existing situation
- Quantified metrics showing the problem
- Limitations and constraints
- Stakeholders affected
Problem Impact:
- Business impact (revenue, customer experience, velocity)
- Technical impact (performance, maintainability, scalability)
- Organizational impact (team productivity, hiring ability)
- Financial impact (costs, efficiency gains/losses)
Root Cause Analysis:
- What is the fundamental cause?
- Is this a technical, organizational, or process issue?
- What contributes to the problem?
Example
Current State:
- Monolithic Spring Boot application (500k LOC)
- Single PostgreSQL database (1.2 TB)
- 120-second integration test suite
- Deployment requires maintenance window
- Single team of 12 engineers
- Coordination overhead increasing
Problem Impact:
- Business: Feature delivery slowed from 2-3 features/week to 1/week
- Technical: Database queries > 100ms affecting user experience
- Organizational: Team size (12) approaching monolith scaling limits
- Financial: Infrastructure costs $500k/year; efficiency declining
Root Cause:
- Monolithic architecture creates tight coupling
- Shared database prevents independent scaling
- Shared deployment creates release coordination overhead
Section 3: Proposed Solution
Describe your proposed approach:
High-Level Design:
[ASCII diagram or description of architecture]
Technology Choices:
- Technology 1: [Name, version]
Rationale: [Why this choice]
Alternatives considered: [Why not others]
- Technology 2: [Name, version]
Rationale: [Why this choice]
Alternatives considered: [Why not others]
Implementation Approach:
1. Phase 1 (Timeline): [Specific objectives and deliverables]
2. Phase 2 (Timeline): [Specific objectives and deliverables]
3. Phase 3 (Timeline): [Specific objectives and deliverables]
Rollback Strategy:
- If issues arise, how will we revert?
- What is the rollback timeline?
- What triggers rollback?
Example
High-Level Design:
┌─────────────────────────────────────┐
│ API Gateway (Kong/Nginx) │
├──────────────┬──────────────┬────────┤
│ Order │ Product │Customer │
│ Service │ Service │Service │
│ (Port 3001) │ (Port 3002) │(Port3003)
├──────────────┼──────────────┼────────┤
│ PostgreSQL │ MongoDB │Redis │
│ (Orders) │ (Catalog) │(Cache) │
└──────────────┴──────────────┴────────┘
│ │ │
└──────────────┼──────────────┘
▼
┌─────────────────┐
│ Kafka (Events) │
└─────────────────┘
Technology Choices:
- Kubernetes: Latest stable (v1.29)
Rationale: Industry standard, operator ecosystem
Alternatives: Docker Swarm (simpler but less flexible), ECS (AWS lock-in)
- Kafka: v3.6
Rationale: High throughput, fault-tolerant, event sourcing capable
Alternatives: RabbitMQ (complex guarantees), AWS SNS/SQS (vendor lock-in)
- PostgreSQL + MongoDB: Polyglot
Rationale: Relational for orders (ACID), document for catalog (flexibility)
Alternatives: Single database (performance trade-offs)
Implementation Approach:
1. Phase 1 (Months 1-2): Extract Order Service
- Create order microservice repo
- Migrate order tables to dedicated PostgreSQL
- Deploy to staging Kubernetes cluster
- Implement API gateway routing
2. Phase 2 (Months 3-4): Extract Product Service
- Create product microservice
- Migrate catalog to MongoDB
- Full-text search via Elasticsearch
- Update API gateway rules
3. Phase 3 (Months 5-6): Extract Customer Service
- Create customer microservice
- Migrate customer data to PostgreSQL replica
- Implement customer caching layer
- Cutover to production
Rollback Strategy:
- Keep monolith running in parallel during Phase 1-2
- API Gateway routes 90% to services, 10% to monolith
- Cutover to 100% services only after 2 weeks of stability
- If P0 production incident: Reroute traffic back to monolith (15 min)
Section 4: Architecture Principles Alignment
Assess alignment with organizational architecture principles:
Principle: [Principle Name]
Current Alignment: [Aligned/Misaligned/Neutral]
Impact: [How does this proposal affect alignment]
Tradeoffs: [What are we accepting/rejecting]
[Repeat for each applicable principle]
Example
Principle: Cloud-First
Current Alignment: Misaligned (on-premises deployment)
Impact: This proposal moves workloads to Kubernetes on cloud
Tradeoffs: Accept: operational complexity gains; Reject: CapEx savings
Principle: Data as Asset
Current Alignment: Misaligned (monolithic data model)
Impact: Polyglot persistence provides flexibility but complicates queries
Tradeoffs: Accept: independent scaling; Reject: unified analytics views
Principle: Security by Default
Current Alignment: Aligned (monolith has security controls)
Impact: Microservices require service mesh for security (mTLS, policies)
Tradeoffs: Accept: consistent security across services; Reject: operational overhead
Section 5: Risk Assessment
Identify and assess risks:
Risk Matrix:
┌─────────────────┬────────────┬────────────┬───────────┐
│ Risk │ Likelihood │ Impact │ Severity │
├─────────────────┼────────────┼────────────┼───────────┤
│ Risk 1 │ High (8/10)│ High ($1M) │ Critical │
│ Mitigation │ [Strategy] │ [Timeline] │ │
├─────────────────┼────────────┼────────────┼───────────┤
│ Risk 2 │ Med (5/10) │ Med ($200k)│ Major │
│ Mitigation │ [Strategy] │ [Timeline] │ │
└─────────────────┴────────────┴────────────┴───────────┘
Risk Monitoring:
- How will we detect if a risk is materializing?
- What is the escalation process?
- What decision points exist?
Example
Risk: Increased operational complexity
Likelihood: High (9/10) - new platform, new tools
Impact: High - team struggle, support costs increase
Severity: Critical
Mitigation:
1. Hire DevOps engineer (Month 1)
2. Implement automated deployments (Month 2)
3. Weekly training sessions (Months 1-4)
Timeline: 4 months to full capability
Risk: Data consistency issues
Likelihood: Medium (6/10) - eventual consistency is new paradigm
Impact: Medium ($100k in lost revenue if unresolved)
Severity: Major
Mitigation:
1. Implement saga pattern for transactions (Month 2)
2. Eventual consistency testing (Month 3)
3. Monitoring dashboard for sync issues (Month 3)
Timeline: 3 months to full stability
Risk: Performance degradation
Likelihood: Medium (5/10) - network calls replace in-process
Impact: High (customer experience)
Severity: Major
Mitigation:
1. Implement API caching layer (Month 1)
2. Service mesh latency optimization (Month 2)
3. Load testing (Month 2-3)
Timeline: 3 months to performance targets
Section 6: Resource & Timeline
Specify resource requirements and timeline:
Team Composition:
- [Role]: [Count] (starting Month X)
- [Role]: [Count] (starting Month X)
- [Role]: [Count] (starting Month X)
Budget:
- Engineering effort: X FTE × Y months = $Z
- Infrastructure costs: [Hardware/cloud costs]
- Training & tooling: $X
- Total: $Y
Timeline:
- Month 1: [Deliverables & milestones]
- Month 2: [Deliverables & milestones]
- Month 3: [Deliverables & milestones]
[...etc...]
Dependencies:
- Infrastructure changes required: [List]
- Technology decisions needed: [List]
- Organizational approvals: [List]
Example
Team Composition:
- 1 Solution Architect: Design and governance (M1-M6, full-time)
- 2 Platform Engineers: Infrastructure and DevOps (M1-M6, full-time)
- 1 Security Engineer: Service-to-service auth, secrets mgmt (M1-M3, 50%)
- 3 Backend Engineers: Microservices implementation (M2-M6, full-time)
- 1 QA Engineer: Testing and automation (M2-M6, 80%)
Budget:
- Engineering: 7.8 FTE × 6 months × $150k/year = $585k
- AWS infrastructure: $40k (Kubernetes, managed databases)
- Tools (monitoring, CI/CD): $15k
- Training: $10k
- Total: $650k
Timeline:
- Month 1: Infrastructure setup, Order Service extraction, API gateway
- Month 2: Order Service optimization, Product Service extraction
- Month 3: Customer Service extraction, integration testing
- Month 4: Load testing, performance tuning, security hardening
- Month 5: Canary deployment to production (10% traffic)
- Month 6: Full production cutover, monitoring stabilization
Dependencies:
- AWS infrastructure: VPC, RDS, EKS cluster setup (Week 1)
- Kubernetes training: 3-day workshop for all backend engineers
- Security review: mTLS/service-to-service auth approval
- Database replication: PostgreSQL logical replication setup
Section 7: Success Criteria
Define measurable success metrics:
Technical Metrics:
- Metric 1: Target [Unit] by [Date]
- Metric 2: Target [Unit] by [Date]
- Metric 3: Target [Unit] by [Date]
Business Metrics:
- Metric 1: Target [Unit] by [Date]
- Metric 2: Target [Unit] by [Date]
Operational Metrics:
- Metric 1: Target [Unit] by [Date]
- Metric 2: Target [Unit] by [Date]
Success Definition:
- When do we declare this initiative a success?
- What would constitute failure requiring rollback?
Example
Technical Metrics:
- API latency (P95): < 200ms (current: 800ms) by Month 6
- Deployment frequency: Daily (current: 2 weeks) by Month 4
- Test coverage: > 85% (current: 62%) by Month 5
- Database query latency: < 50ms (current: 120ms) by Month 5
Business Metrics:
- Feature velocity: 2+ features/week (current: 1/week) by Month 4
- Time-to-market: 1 day (current: 2 weeks) by Month 6
- Customer-reported issues: -40% (current: 150/month) by Month 6
Operational Metrics:
- MTTR (mean time to recovery): < 15 min (current: 45 min) by Month 6
- Infrastructure costs: $45k/month (current: $40k, acceptable due to features) by Month 6
- Runbook completion time: < 30 min (new metric) by Month 4
Success Definition:
- Initiative succeeds if all Technical & Business metrics met by Month 6
- Failure rollback trigger: > 3 P0 incidents in production related to microservices
- If triggered, revert to monolith within 24 hours
Section 8: Alternative Approaches
Present alternatives considered:
Alternative 1: [Name]
- Approach: [Description]
- Pros: [List]
- Cons: [List]
- Cost: [Estimate]
- Timeline: [Estimate]
- Rationale for rejection: [Why not this one]
Alternative 2: [Name]
- [Repeat structure]
Why our proposed solution is best:
[Justification]
Example
Alternative 1: Do Nothing (Optimize Monolith)
- Approach: Improve existing monolith performance (database optimization, caching)
- Pros: Lower cost ($100k), shorter timeline (3 months), lower risk
- Cons: Fundamental scalability limits remain, team coordination problems persist
- Cost: $100k
- Timeline: 3 months
- Rationale: Addresses symptoms, not root cause; team growth blocked
Alternative 2: Cloud-Native Serverless (AWS Lambda)
- Approach: Decompose into Lambda functions with API Gateway
- Pros: No infrastructure management, automatic scaling, pay-per-use
- Cons: Vendor lock-in (AWS), cold start issues, complex debugging, limited stateful options
- Cost: $60k initial + $200k/year operations
- Timeline: 5 months
- Rationale: Trade operational overhead for vendor lock-in; less control over performance
Why proposed solution is best:
- Balances control and simplicity (Kubernetes is industry standard)
- Enables independent team scaling without vendor lock-in
- Technology choices support both current and future growth
- Timeline and cost acceptable for strategic importance
- Proven approach with strong community ecosystem
Section 9: Architecture Review Comments
Space for ARB comments and decisions:
Reviewer: [Name]
Date: [Date]
Comment: [Specific feedback or questions]
---
Reviewer: [Name]
Date: [Date]
Comment: [Specific feedback or questions]
---
Final Decision:
☐ Approved
☐ Approved with Conditions: [List conditions]
☐ Request Modifications: [List required changes]
☐ Rejected: [Reason]
Conditions/Modifications:
1. [Specific requirement]
2. [Specific requirement]
3. [Specific requirement]
Section 10: Implementation Tracking
Track implementation progress:
Milestone 1: [Name]
- Target date: [Date]
- Status: [Not started/In progress/Complete]
- Completion: X%
- Issues: [List blockers]
[Repeat for each milestone]
Overall Progress: [Percentage]
On track: [Yes/No/At risk]
Next review: [Date]
ARB Submission Checklist
- Problem statement is clear and quantified
- Solution is well-designed with diagrams
- Alignment with architecture principles assessed
- All significant risks identified and mitigated
- Resource requirements and timeline realistic
- Success criteria measurable and achievable
- Alternatives considered and documented
- Stakeholders consulted and aligned
- Executive sponsor identified
- Implementation plan detailed
- Rollback strategy defined
- Monitoring and observability planned
Related Resources
Status: Complete
Last Updated: November 2025
Version: 1.0
Contributors: Architecture Practice Team