cloud-solution-architect

name cloud-solution-architect description Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews. Cloud Solution Architect Overview Design well-architected, production-grade cloud systems following Azure Architecture Center best practices. This skill provides: 10 design principles for Azure applications 6 architecture styles with selection guidance 44 cloud design patterns mapped to WAF pillars Technology choice frameworks for compute, storage, data, messaging Performance antipatterns to avoid Architecture review workflow for systematic design validation Ten Design Principles for Azure Applications # Principle Key Tactics 1 Design for self-healing Retry with backoff, circuit breaker, bulkhead isolation, health endpoint monitoring, graceful degradation 2 Make all things redundant Eliminate single points of failure, use availability zones, deploy multi-region, replicate data 3 Minimize coordination Decouple services, use async messaging, embrace eventual consistency, use domain events 4 Design to scale out Horizontal scaling, autoscaling rules, stateless services, avoid session stickiness, partition workloads 5 Partition around limits Data partitioning (shard/hash/range), respect compute & network limits, use CDNs for static content 6 Design for operations Structured logging, distributed tracing, metrics & dashboards, runbook automation, infrastructure as code 7 Use managed services Prefer PaaS over IaaS, reduce operational burden, leverage built-in HA/DR/scaling 8 Use an identity service Microsoft Entra ID, managed identity, RBAC, avoid storing credentials, zero-trust principles 9 Design for evolution Loose coupling, versioned APIs, backward compatibility, async messaging for integration, feature flags 10 Build for business needs Define SLAs/SLOs, establish RTO/RPO targets, domain-driven design, cost modeling, composite SLAs Architecture Styles Style Description When to Use Key Services N-tier Horizontal layers (presentation, business, data) Traditional enterprise apps, lift-and-shift App Service, SQL Database, VNets Web-Queue-Worker Web frontend → message queue → backend worker Moderate-complexity apps with long-running tasks App Service, Service Bus, Functions Microservices Small autonomous services, bounded contexts, independent deploy Complex domains, independent team scaling AKS, Container Apps, API Management Event-driven Pub/sub model, event producers/consumers Real-time processing, IoT, reactive systems Event Hubs, Event Grid, Functions Big data Batch + stream processing pipeline Analytics, ML pipelines, large-scale data Synapse, Data Factory, Databricks Big compute HPC, parallel processing Simulations, modeling, rendering, genomics Batch, CycleCloud, HPC VMs Selection Criteria Domain complexity → Microservices (high), N-tier (low-medium) Team autonomy → Microservices (independent teams), N-tier (single team) Data volume → Big data (TB+), others (GB) Latency requirements → Event-driven (real-time), Web-Queue-Worker (tolerant) Cloud Design Patterns 44 patterns organized by primary concern. WAF pillar mapping: R =Reliability, S =Security, CO =Cost Optimization, OE =Operational Excellence, PE =Performance Efficiency. Messaging & Communication Pattern Summary Pillars Asynchronous Request-Reply Decouple request/response with polling or callbacks R, PE Claim Check Split large messages; store payload separately, pass reference R, PE Choreography Services coordinate via events without central orchestrator R, OE Competing Consumers Multiple consumers process messages from shared queue concurrently R, PE Messaging Bridge Connect incompatible messaging systems R, OE Pipes and Filters Decompose complex processing into reusable filter stages R, OE Priority Queue Prioritize requests so higher-priority work is processed first R, PE Publisher/Subscriber Decouple senders from receivers via topics/subscriptions R, PE Queue-Based Load Leveling Buffer requests with a queue to smooth intermittent loads R, PE Sequential Convoy Process related messages in order while allowing parallel groups R, PE Reliability & Resilience Pattern Summary Pillars Bulkhead Isolate resources per workload to prevent cascading failure R Circuit Breaker Stop calling a failing service; fail fast to protect resources R Compensating Transaction Undo previously committed steps when a later step fails R Health Endpoint Monitoring Expose health checks for load balancers and orchestrators R, OE Leader Election Coordinate distributed instances by electing a leader R Retry Handle transient faults by retrying with exponential backoff R Saga Manage data consistency across microservices with compensating transactions R Scheduler Agent Supervisor Coordinate distributed actions with retry and failure handling R Data Management Pattern Summary Pillars Cache-Aside Load data on demand into cache from data store PE CQRS Separate read and write models for independent scaling PE, R Event Sourcing Store state as append-only sequence of domain events R, OE Index Table Create indexes over frequently queried fields in data stores PE Materialized View Pre-compute views over data for efficient queries PE Sharding Distribute data across partitions for scale and performance PE, R Static Content Hosting Serve static content from cloud storage/CDN directly PE, CO Valet Key Grant clients limited direct access to storage resources S, PE Design & Structure Pattern Summary Pillars Ambassador Offload cross-cutting concerns to a helper sidecar proxy OE Anti-Corruption Layer Translate between new and legacy system models OE, R Backends for Frontends Create separate backends per frontend type (mobile, web, etc.) OE, PE Compute Resource Consolidation Combine multiple workloads into fewer compute instances CO External Configuration Store Externalize configuration from deployment packages OE Sidecar Deploy helper components alongside the main service OE Strangler Fig Incrementally migrate legacy systems by replacing pieces OE, R Security & Access Pattern Summary Pillars Federated Identity Delegate authentication to an external identity provider S Gatekeeper Protect services using a dedicated broker that validates requests S Quarantine Isolate and validate external assets before allowing use S Rate Limiting Control consumption rate of resources by consumers R, S Throttling Control resource consumption to sustain SLAs under load R, PE Deployment & Scaling Pattern Summary Pillars Deployment Stamps Deploy multiple independent copies of application components R, PE Edge Workload Configuration Configure workloads differently across diverse edge devices OE Gateway Aggregation Aggregate multiple backend calls into a single client request PE Gateway Offloading Offload shared functionality (SSL, auth) to a gateway OE, S Gateway Routing Route requests to multiple backends using a single endpoint OE Geode Deploy backends to multiple regions for active-active serving R, PE See Design Patterns Reference for detailed implementation guidance. Technology Choices Decision Framework For each technology area, evaluate: requirements → constraints → tradeoffs → select . Area Key Options Selection Criteria Compute App Service, Functions, Container Apps, AKS, VMs, Batch Hosting model, scaling, cost, team skills Storage Blob Storage, Data Lake, Files, Disks, Managed Lustre Access patterns, throughput, cost tier Data stores SQL Database, Cosmos DB, PostgreSQL, Redis, Table Storage Consistency model, query patterns, scale Messaging Service Bus, Event Hubs, Event Grid, Queue Storage Ordering, throughput, pub/sub vs queue Networking Front Door, Application Gateway, Load Balancer, Traffic Manager Global vs regional, L4 vs L7, WAF AI services Azure OpenAI, AI Search, AI Foundry, Document Intelligence Model needs, data grounding, orchestration Containers Container Apps, AKS, Container Instances Operational control vs simplicity See Technology Choices Reference for detailed decision trees. Best Practices Practice Key Guidance API design RESTful conventions, resource-oriented URIs, HATEOAS, versioning via URL path or header API implementation Async operations, pagination, idempotent PUT/DELETE, content negotiation, ETag caching Autoscaling Scale on metrics (CPU, queue depth, custom), cool-down periods, predictive scaling, scale-in protection Background jobs Use queues or scheduled triggers, idempotent processing, poison message handling, graceful shutdown Caching Cache-aside pattern, TTL policies, cache invalidation strategies, distributed cache for multi-instance CDN Static asset offloading, cache-busting with versioned URLs, geo-distribution, HTTPS enforcement Data partitioning Horizontal (sharding), vertical, functional partitioning; partition key selection for even distribution Partitioning strategies Hash-based, range-based, directory-based; rebalancing approach, cross-partition query avoidance Host name preservation Preserve original host header through proxies/gateways for cookies, redirects, auth flows Message encoding Schema evolution (Avro/Protobuf), backward/forward compatibility, schema registry Monitoring & diagnostics Structured logging, distributed tracing (W3C Trace Context), metrics, alerts, dashboards Transient fault handling Retry with exponential backoff + jitter, circuit breaker, idempotency keys, timeout budgets See Best Practices Reference for implementation details. Performance Antipatterns Avoid these common patterns that degrade performance under load: Antipattern Problem Fix Busy Database Offloading too much processing to the database Move logic to application tier, use caching Busy Front End Resource-intensive work on frontend request threads Offload to background workers/queues Chatty I/O Many small I/O requests instead of fewer large ones Batch requests, use bulk APIs, buffer writes Extraneous Fetching Retrieving more data than needed Project only required fields, paginate, filter server-side Improper Instantiation Recreating expensive objects per request Use singletons, connection pooling, HttpClientFactory Monolithic Persistence Single data store for all data types Polyglot persistence — right store for each workload No Caching Repeatedly fetching unchanged data Cache-aside pattern, CDN, output caching, Redis Noisy Neighbor One tenant consuming all shared resources Bulkhead isolation, per-tenant quotas, throttling Retry Storm Aggressive retries overwhelming a recovering service Exponential backoff + jitter, circuit breaker, retry budgets Synchronous I/O Blocking threads on I/O operations Async/await, non-blocking I/O, reactive streams Mission-Critical Design For workloads targeting 99.99%+ SLO , address these design areas: Design Area Key Considerations Application platform Multi-region active-active, availability zones, Container Apps or AKS with zone redundancy Application design Stateless services, idempotent operations, graceful degradation, bulkhead isolation Networking Azure Front Door (global LB), DDoS Protection, private endpoints, redundant connectivity Data platform Multi-region Cosmos DB, zone-redundant SQL, async replication, conflict resolution Deployment & testing Blue-green deployments, canary releases, chaos engineering, automated rollback Health modeling Composite health scores, dependency health tracking, automated remediation, SLI dashboards Security Zero-trust, managed identity everywhere, key rotation, WAF policies, threat modeling Operational procedures Automated runbooks, incident response playbooks, game days, postmortems See Mission-Critical Reference for detailed guidance. Well-Architected Framework (WAF) Pillars Every architecture decision should be evaluated against all five pillars: | Pillar | Focus | Key Questions |