# Architecture Playbook — Fernando F. Azevedo Senior playbook for AWS, financial systems, event-driven architecture, GenAI guardrails, data platforms, DevSecOps and architecture communication. - HTML: https://fernando.moretes.com/architecture - E-book: https://fernando.moretes.com/ebooks/elevador-de-arquitetura-bancos - E-book Markdown: https://fernando.moretes.com/ebooks/elevador-de-arquitetura-bancos/ebook.md ## Principles ### Make trade-offs explicit Good architecture is not a diagram. It is a set of intentional decisions across risk, cost, reliability, security, delivery speed and team maturity. - C4 model - ADRs - SADs - Well-Architected reviews ### Design for regulated operations Financial systems need architecture that keeps auditability, identity, data protection, resilience and operational readiness visible from day one. - Zero Trust - IAM/KMS/WAF - PCI-DSS - LGPD ### Prefer evolutionary modernization Modernization works best when domain boundaries, platform capabilities and migration increments reduce risk while keeping business value moving. - DDD - BFF - microservices - event-driven integration ### Use AI with guardrails AI must be useful, observable and bounded. Abuse protection, cost controls, privacy posture and fallback behavior are part of the architecture. - Bedrock - RAG - rate limits - S3 telemetry ## Well-Architected Lens ### Operational excellence Architecture should make change safe: telemetry, runbooks, IaC, rollback paths and learning loops after incidents. - SLOs and error budgets - ADRs and runbooks - CI/CD controls - post-incident learning ### Security Identity, network boundaries, data protection and threat modeling must be design inputs, especially in financial environments. - least privilege IAM - KMS and secrets - WAF and rate limits - audit trails ### Reliability Systems should degrade gracefully, isolate failure and prove recovery targets before production pressure arrives. - RTO/RPO - multi-AZ design - idempotency - backoff and circuit breakers ### Performance efficiency Pick compute, storage and integration patterns from workload shape, latency needs, access patterns and team maturity. - load testing - caching - async flows - right-sized data stores ### Cost optimization Cost is an architecture signal. Good systems expose unit economics, limits, quotas and waste early. - tagging - budgets - storage lifecycle - serverless where it fits ### Sustainability Efficient architectures reduce idle capacity, unnecessary data movement and retention that no longer creates value. - managed services - data retention - right-sizing - efficient workloads ## Architecture Patterns ### Event-driven banking integration **Best for:** Decoupling domains, reducing synchronous dependencies and enabling auditable business events. **Watch-out:** Without schema discipline and observability, event-driven systems become distributed uncertainty. - Kafka/MSK - EventBridge - Schema Registry - OpenTelemetry ### BFF plus microfrontend boundaries **Best for:** Digital channels that need independent evolution, clear ownership and UX-specific APIs. **Watch-out:** BFFs should protect product flows, not become another shared monolith. - NestJS - Next.js - React - Clean Architecture ### Serverless control plane **Best for:** Low-ops workflows, scheduled automation, AI pipelines, newsletters and event-driven background jobs. **Watch-out:** Cold starts, retries, idempotency and IAM scoping still need deliberate design. - Lambda - DynamoDB - S3 - EventBridge ### AI assistant with retrieval **Best for:** Explaining domain knowledge, professional profiles, internal knowledge bases and decision support. **Watch-out:** Treat prompt injection, data leakage, runaway cost and hallucination as architecture risks. - Amazon Bedrock - Titan embeddings - RAG - DynamoDB limits ### Observability-first services **Best for:** Microservices and distributed teams that need fast diagnosis, reliability learning and SLO conversations. **Watch-out:** Dashboards do not create reliability unless traces, logs, metrics and ownership are connected. - OpenTelemetry - Datadog - CloudWatch - SLOs ### GitOps and IaC delivery **Best for:** Reducing drift, improving reviewability and aligning infrastructure changes with engineering flow. **Watch-out:** Automation without policy and rollback strategy can accelerate mistakes. - Terraform - GitHub Actions - ArgoCD - AWS CDK ## AWS Decision Domains ### Compute and containers Lambda, ECS/Fargate, EKS, EC2 and Batch should be selected by runtime profile, operational model, latency, portability and deployment cadence. - Lambda for event control planes - ECS/Fargate for service teams - EKS for platform-scale Kubernetes ### Data and storage Start from access patterns, consistency, retention, analytical needs and ownership. The database choice is a product and architecture decision. - DynamoDB single-table design - Aurora for relational domains - S3 lakehouse and lifecycle ### Integration and streaming Prefer clear contracts between domains. Choose orchestration, choreography, queues or streams according to ordering, replay, fanout and audit needs. - EventBridge for domain events - SQS for buffering - MSK/Kinesis for high-volume streams ### Security and governance Security architecture should be measurable: identity boundaries, encryption, detective controls, policy-as-code and evidence for audits. - Organizations and SCPs - CloudTrail and Config - Security Hub and GuardDuty ### AI, data products and GenAI AI workloads need evaluation, abuse controls, privacy boundaries, model choice, vector strategy and cost guardrails before scaling usage. - Bedrock guardrails - RAG evaluation - token budgets and throttling ### Observability and operations Logs, metrics, traces and business events must connect technical health to product impact and incident response. - OpenTelemetry - CloudWatch - Datadog - dashboards tied to SLOs ## Senior Material Map ### Architecture foundations Architectural characteristics, styles, trade-offs, diagrams, ADRs, governance and communication with engineering and business stakeholders. - Fundamentals of Software Architecture - Software Architecture: The Hard Parts ### Distributed data systems Consistency, replication, partitioning, event logs, storage engines, schemas, data products and the consequences of distributed ownership. - Designing Data-Intensive Applications - Data Mesh - event sourcing and streaming ### AWS cloud-native architecture Well-Architected thinking, decision guides, reference architectures, migration increments, resilience patterns and cost-aware service selection. - AWS Well-Architected Framework - AWS Architecture Center - System Design on AWS ### Modernization and platform engineering Evolutionary architecture, paved roads, GitOps, Terraform/CDK, CI/CD, golden paths, developer experience and operational maturity. - Building Evolutionary Architectures - Cloud Application Architecture Patterns ### AI and intelligent systems RAG, agents, Bedrock, evaluation, prompt-injection defense, rate limits, observability, MLOps/AIOps and human-in-the-loop operations. - AWS for Solutions Architects - Bedrock patterns - secure AI architecture ## Decision Questions - What business risk does this architecture reduce? - Where are the domain boundaries and who owns them? - What fails closed, what fails open and what degrades gracefully? - How do we prove security, reliability and cost posture after deploy? - Which decision is reversible, and which one creates long-term coupling? - What telemetry will help the team learn from production? ## Reading Roadmap ### 1. Foundation: Fundamentals of Software Architecture, 2nd Edition Builds the base for styles, architectural characteristics, trade-off analysis, diagrams, governance and the daily work of an architect. ### 2. Trade-offs: Software Architecture: The Hard Parts Deepens the difficult decisions around service boundaries, coupling, distributed data, granularity and the cases where there is no perfect answer. ### 3. Data-intensive systems: Designing Data-Intensive Applications, 2nd Edition Strengthens reasoning about storage, consistency, replication, streams, scalability and data architecture for large systems. ### 4. Cloud patterns: Cloud Application Architecture Patterns Connects cloud-native patterns, modernization and migration with practical application architecture independent of a single provider. ### 5. AWS practice: AWS Well-Architected, AWS for Solutions Architects and System Design on AWS Turns system design into AWS decisions across reliability, security, performance, cost, sustainability, GenAI, MLOps and platform operations. ### 6. Evolution: Building Evolutionary Architectures, Building Microservices and Designing Distributed Systems Adds fitness functions, service evolution, Kubernetes-oriented patterns and pragmatic modernization of complex systems over time.