Introduction

Most Java systems don’t fail because a single class was written badly. They fail because the boundaries between classes were drawn in the wrong place — a database library leaks into business logic, a payment provider’s SDK shows up in twelve unrelated files, and changing one third-party dependency means touching half the codebase.

Designing for change is not about predicting the future correctly. It’s about isolating what varies so that when it inevitably does vary — a new payment provider, a swapped database, a different message broker — the blast radius is one adapter, not the whole system.

Introduction

Python’s flexibility makes it easy to wire everything together directly — call the requests library from inside your business logic, import the ORM model straight into your pricing rules, instantiate a third-party SDK client wherever it’s needed. It works, right up until the payment provider changes, or you want a fast unit test that doesn’t need a live database.

Designing for change means drawing boundaries around the parts of the system likely to move — third-party services, storage, transport — so that a swap or a test double touches one small adapter instead of rippling through the codebase.

Introduction

For a long time, Java was associated with large, monolithic enterprise applications running on heavyweight application servers.

Then the industry shifted toward:

• Microservices
• Containers
• Cloud-native architectures
• Kubernetes

Many assumed Java would struggle to adapt.

Instead, Java evolved — and in many cases, thrived.

From Monoliths to Microservices

Early Java enterprise applications often relied on:

• Large EAR/WAR deployments
• Heavy application servers
• Centralized databases

Modern Java architectures look very different:

• Small, focused services
• Independent deployments
• Stateless APIs
• Horizontal scalability

Frameworks like Spring Boot dramatically simplified Java service development by removing boilerplate and enabling rapid startup.