Microservices Architecture: Lessons from 20+ Years of Enterprise Delivery

After two decades of delivering technology solutions across Telecom, Healthcare, Banking, and Energy sectors, I've learned that microservices architecture is not a silver bullet—it's a strategic trade-off that requires careful consideration of your organization's maturity, team structure, and business requirements.

The Monolith Decomposition Myth

Most enterprises don't start with microservices—they evolve into them. The common mistake I've witnessed repeatedly is decomposing based on technical layers (UI service, business logic service, data service) rather than business domains.

Domain-Driven Decomposition

At Vitrifi, we applied Domain-Driven Design principles to identify service boundaries:

• Bounded Contexts: Each service owns a specific business capability with clear boundaries
• Ubiquitous Language: Teams speak the same language as business stakeholders
• Context Mapping: Explicitly define relationships between services (Anti-corruption layers, Shared Kernels)

The result? Services that align with how the business thinks, making changes faster and reducing cross-team coordination overhead.

When to Keep the Monolith

Not every system needs microservices. Consider keeping a monolith when:

• Your team is small (under 10 developers)
• The domain is well-understood and stable
• You lack operational maturity for distributed systems
• Time-to-market is critical and you can't afford the infrastructure overhead

Event-Driven Patterns That Work

In my work at Vitrifi and Vattenfall, event-driven architecture proved essential for handling scale. But choosing between messaging technologies requires understanding their trade-offs.

NATS vs Kafka: A Practical Comparison

Choose NATS when:

• Sub-millisecond latency is critical
• Request-reply patterns dominate your communication
• You need simpler operational requirements
• Edge deployments with resource constraints

Choose Kafka when:

• You need durable message storage and replay capabilities
• Event sourcing is a core architectural pattern
• Stream processing with exactly-once semantics is required
• You have complex consumer group requirements

At Vitrifi, we used both: NATS for real-time service mesh communication and Kafka for event sourcing and analytics pipelines.

Event Design Principles

Well-designed events are crucial for maintainability:

1. Events should be facts, not commands: "OrderPlaced" not "PlaceOrder"
2. Include enough context: Recipients shouldn't need to call back for basic information
3. Version your schemas: Use schema registries and plan for evolution
4. Avoid event chains: Long chains of events become hard to debug

Observability: Your Safety Net

Distributed systems fail in distributed ways. Without proper observability, you're flying blind.

The Three Pillars in Practice

• Metrics: Track the RED metrics (Rate, Errors, Duration) for every service
• Logs: Structured JSON logs with correlation IDs that span service boundaries
• Traces: Distributed tracing with OpenTelemetry to visualize request flow

Correlation is Key

Every request entering your system should receive a correlation ID that flows through all downstream services. This single practice has saved countless debugging hours.

Data Management Challenges

Microservices mean distributed data, and distributed data means complexity.

Database Per Service

Each service should own its data. Cross-service data access happens through APIs, not shared databases. This seems obvious until you need to run a report that joins data from five services.

Saga Patterns for Transactions

Without distributed transactions, you need sagas. We've successfully used both:

• Choreography: Services react to events autonomously (simpler, but harder to trace)
• Orchestration: A coordinator manages the workflow (easier to understand, but creates coupling)

Choose orchestration for critical business processes where visibility matters, and choreography for loosely coupled, independent flows.

Key Takeaways

1. Start with clear domain boundaries: Invest time in understanding the business domain before drawing service boundaries
2. Invest in observability from day one: Retrofitting observability is painful; build it in from the start
3. Accept eventual consistency where it makes sense: Not every operation needs immediate consistency
4. Don't microservice everything: The best architecture is the simplest one that meets your requirements
5. Automate ruthlessly: Without CI/CD, testing, and deployment automation, microservices become a burden
6. Build for failure: Assume every network call can fail and design accordingly