cloud-solution-architect

by microsoft

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SKILL.md About

---
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](./references/design-patterns.md) 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](./references/technology-choices.md) 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](./references/best-practices.md) 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](./references/mission-critical.md) for detailed guidance.

---

## Well-Architected Framework (WAF) Pillars

Every architecture decision should be evaluated against all five pillars:

| Pillar | Focus | Key Questions |
|--------|-------|---------------|
| **Reliability** | Resiliency, availability, disaster recovery | What is the RTO/RPO? How does it handle failures? Is there redundancy? |
| **Security** | Threat protection, identity, data protection | Is identity managed? Is data encrypted? Are there network controls? |
| **Cost Optimization** | Cost management, efficiency, right-sizing | Is compute right-sized? Are there reserved instances? Is there waste? |
| **Operational Excellence** | Monitoring, deployment, automation | Is deployment automated? Is there observability? Are there runbooks? |
| **Performance Efficiency** | Scaling, load testing, performance targets | Can it scale horizontally? Are there performance baselines? Is caching used? |

### WAF Tradeoff Matrix

| Optimizing for... | May impact... |
|-------------------|---------------|
| Reliability (redundancy) | Cost (more resources) |
| Security (isolation) | Performance (added latency) |
| Cost (consolidation) | Reliability (shared failure domains) |
| Performance (caching) | Cost (cache infrastructure), Reliability (stale data) |

---

## Architecture Review Workflow

When reviewing or designing a system, follow this structured approach:

### Step 1: Identify Requirements

```
Functional: What must the system do?
Non-functional:
  - Availability target (e.g., 99.9%, 99.99%)
  - Latency requirements (p50, p95, p99)
  - Throughput (requests/sec, messages/sec)
  - Data residency and compliance
  - Recovery targets (RTO, RPO)
  - Cost constraints
```

### Step 2: Select Architecture Style

Match requirements to architecture style using the selection criteria table above.

### Step 3: Choose Technology Stack

Use the technology choices decision framework. Prefer managed services (PaaS) over IaaS.

### Step 4: Apply Design Patterns

Select relevant patterns from the 44 cloud design patterns based on identified concerns.

### Step 5: Address Cross-Cutting Concerns

- **Identity & access** — Microsoft Entra ID, managed identity, RBAC
- **Monitoring** — Application Insights, Azure Monitor, Log Analytics
- **Security** — Network segmentation, encryption at rest/in transit, Key Vault
- **CI/CD** — GitHub Actions, Azure DevOps Pipelines, infrastructure as code

### Step 6: Validate Against WAF Pillars

Review each pillar systematically. Document tradeoffs explicitly.

### Step 7: Document Decisions

Use Architecture Decision Records (ADRs):

```markdown
# ADR-NNN: [Decision Title]

## Status: [Proposed | Accepted | Deprecated]

## Context
[What is the issue we're addressing?]

## Decision
[What did we decide and why?]

## Consequences
[What are the positive and negative impacts?]
```

---

## References

- [Design Patterns Reference](./references/design-patterns.md) — Detailed pattern implementations
- [Technology Choices Reference](./references/technology-choices.md) — Decision trees for Azure services
- [Best Practices Reference](./references/best-practices.md) — Implementation guidance
- [Mission-Critical Reference](./references/mission-critical.md) — High-availability design

---

## Source

Content derived from the [Azure Architecture Center](https://learn.microsoft.com/en-us/azure/architecture/) — Microsoft's official guidance for cloud solution architecture on Azure. Covers design principles, architecture styles, cloud design patterns, technology choices, best practices, performance antipatterns, mission-critical design, and the Well-Architected Framework.