Microservices Architecture in Practice

Microservices Architecture in Practice Microservices split a large system into small, independent services. Each service owns a specific business capability and can be developed, deployed, and scaled separately. In practice, this approach speeds delivery and fosters autonomy, but it also adds coordination work, especially around contracts, data, and observability. Start by mapping the domain. Work with product teams to define service boundaries, then group related capabilities into autonomous units. Keep services small and cohesive, with stable APIs and minimal shared state. Avoid cross-cutting dependencies that create tight coupling. ...

September 22, 2025 · 2 min · 288 words

Microservices vs Monoliths: Choosing an Architecture

Microservices vs Monoliths: Choosing an Architecture Choosing an architecture shapes how you build, test, and release software. There is no one perfect answer; the best path fits your team and your product. Monoliths work well for small teams and stable goals. A single codebase is easier to test, deploy, and learn from. If requirements stay simple and data stays tightly connected, a monolith can move quickly. Microservices give independence and scalability. They suit larger teams, diverse tech choices, or services that must scale separately. Each service owns its data and API, and you can deploy changes without touching the whole system. They demand strong automation, clear contracts, and good monitoring. ...

September 21, 2025 · 2 min · 320 words

Microservices vs Monoliths: Choosing the Right Architecture

Microservices vs Monoliths: Choosing the Right Architecture Choosing between microservices and a monolith is not only a technical decision. It is also about teams, goals, and risk. A monolith is a single codebase that ships as one deployable unit. Microservices divide the system into small, independently deployable services that communicate over a network. Each option has benefits and costs. Monoliths shine when your product is simple, your team is small, and speed matters. You can ship updates quickly with less coordination. Testing can be straightforward because all code runs in one process. Latency is lower, and you have a single data model to reason about. On the downside, a growing app can become hard to change, and a single bug might touch many parts. ...

September 21, 2025 · 2 min · 332 words