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Java Systems That Handle Heavy Business Load

Spring Boot APIs, microservices and enterprise backends in Java, plus modernisation of older Java applications onto current versions and cloud platforms.

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OrderController.java
Sample code

Java development for dependable enterprise backends

Java has run core banking, insurance, logistics and telecom systems for decades, and modern Java is far more pleasant than its reputation suggests. Recent LTS releases bring records, virtual threads and pattern matching, while Spring Boot makes it quick to build production-ready services with security, data access and monitoring built in. The JVM itself is fast, well understood and supported by a huge ecosystem.

Java is the right choice for high-volume transaction processing, complex domain logic, regulated industries and organisations that already run on the JVM. For quick startup MVPs or lightweight APIs, Node.js or Python can get you moving with less setup. For Microsoft-centred shops, .NET is the closer equivalent. We look at your team and existing systems before recommending a stack.

Nexzem builds Spring Boot services with clear domain boundaries, integration tests and observability from the start. We also modernise older Java EE and monolithic applications, upgrading Java versions, splitting out services where it genuinely helps, and moving workloads into containers on AWS, Azure or Google Cloud without disrupting the business that depends on them.

Read Java, the way we write it

A short, idiomatic sample. Scroll and the editor types each part while the note beside it explains why it is written that way.

OrderController.java
Sample code
package com.example.shop;
import org.springframework.web.bind.annotation.*;
// Records give immutable DTOs in one line
record OrderDto(long id, String customer, java.math.BigDecimal total) {}
@RestController
@RequestMapping("/orders")
class OrderController {
private final OrderService service;
// Constructor injection: no field magic, easy to test
OrderController(OrderService service) { this.service = service; }
@GetMapping("/{id}")
OrderDto byId(@PathVariable long id) {
// A ControllerAdvice maps "not found" to a 404
return service.find(id);
}
}

  1. line 5-12

    Records give immutable DTOs in one line

  2. line 13-17

    Constructor injection: no field magic, easy to test

  3. line 18-21

    A ControllerAdvice maps "not found" to a 404

What we build with Java

Java and Spring Boot systems for enterprise backends, microservices and high-volume transaction processing.

  1. 01

    Spring Boot APIs

    REST and event-driven services with Spring Security, Spring Data and OpenAPI docs, built for clean integration with web, mobile and partner systems.

  2. 02

    Microservices Architecture

    Services split along business domains with Kafka messaging, service discovery and distributed tracing, introduced only where independent scaling is genuinely needed.

  3. 03

    Transaction Processing Systems

    Payment, order, billing and ledger backends designed for correctness, idempotency and complete audit trails, so every transaction can be traced and reconciled even under heavy volume.

  4. 04

    Legacy Java Modernisation

    Upgrades from Java 8 and Java EE to current LTS releases and Spring Boot, replacing outdated libraries and app servers step by step.

  5. 05

    Cloud Migration for Java

    Containerisation with Docker and deployment to Kubernetes or managed services, with CI/CD, externalised configuration, health checks and autoscaling rules suited to how each service is actually used.

  6. 06

    Enterprise Integrations

    Connections to ERPs, CRMs, core banking systems and message brokers through APIs, files and queues, with error handling and reconciliation.

  7. 07

    Performance Tuning

    JVM profiling, garbage collection tuning, query optimisation and caching that reduce latency and infrastructure spend on busy services.

Why teams pick Nexzem for Java

The checks every engagement has to pass before we call it done.

.github/PULL_REQUEST_TEMPLATE.md5/5 checked

  • - [x] Proven at scale

    Java and the JVM are trusted for high-volume, business-critical workloads.

  • - [x] Strong typing and tooling

    Compile-time checks and mature IDEs make large codebases safer to change.

  • - [x] Pragmatic architecture

    We split into microservices only where it pays off, avoiding needless operational overhead.

  • - [x] Observability built in

    Metrics, logs and traces are configured from day one, so issues are found fast.

  • - [x] Long-term partnership

    Support plans or dedicated Java engineers billed monthly keep systems current.

Java vs Kotlin vs Node.js for enterprise backends

Java's strengths are the JVM's mature performance, a vast ecosystem led by Spring, excellent tooling and a deep talent pool in enterprises worldwide. Long-term support releases arrive every two years, and recent versions added virtual threads, records, pattern matching and sealed classes, making modern Java far more concise and scalable than the Java 8 code many teams still maintain.

Kotlin runs on the same JVM, interoperates fully with Java and works well with Spring, offering more concise syntax and null safety. Node.js suits I/O-heavy APIs and teams standardizing on TypeScript. Our Kotlin vs Java comparison and microservices vs monolith comparison help frame these choices. For transaction-heavy, long-lived enterprise systems, Java remains one of the safest bets.

The choice also depends on existing systems. Organizations with large Java estates gain more from modernizing them, upgrading versions and adopting Spring Boot practices, than from switching languages, which brings retraining costs and two ecosystems to maintain. A focused modernization program often delivers results within a few months.

How we structure a Spring Boot service

We often begin with a modular monolith: one deployable application with strict module boundaries by business domain, enforced with tools such as Spring Modulith or ArchUnit tests. Modules can later be extracted into separate services if scaling or team ownership demands it, without the early cost of distributed systems.

Data access uses Spring Data JPA or jOOQ with Flyway or Liquibase migrations, APIs are documented with OpenAPI, and Spring Security handles OAuth2 and role-based access. Micrometer and OpenTelemetry provide metrics and traces, JUnit 5 and Testcontainers cover integration tests, and container images are built reproducibly in CI.

  • Modules by business domain with enforced boundaries.
  • Versioned database migrations.
  • Centralized exception handling and consistent error responses.
  • Metrics, traces and structured logs from the first deployment.
  • Architecture tests that stop accidental coupling.
  • Container images built and scanned for vulnerabilities.

Performance tuning Java services

Most Java performance problems come from the database layer: inefficient JPA queries that load related data one row at a time, missing indexes and connection pools sized wrongly for the workload. Enabling query logging in development and profiling with Java Flight Recorder reveals where time actually goes before anyone tunes the JVM.

In containers, set memory limits so the JVM sizes its heap correctly, choose a garbage collector suited to latency needs, such as G1 for general workloads or ZGC for very low pause times, and use virtual threads for services that spend most of their time waiting on I/O. Caching hot reads with Redis or Caffeine often delivers the largest single improvement.

Where startup time matters, such as serverless functions or rapidly scaling services, GraalVM native images can start in milliseconds, at the cost of longer builds and some framework restrictions. Measure before adopting them. Many services gain more from simple tuning than from switching runtimes.

How Java projects run

$ git log --graph --oneline main..delivery

  1. a06d265

    feat: domain and system review

    We map business processes, data flows and existing Java systems.

  2. 1418ea1

    feat: architecture design

    Service boundaries, data ownership, messaging and deployment approach agreed upfront.

  3. 39de04d

    feat: build and integrate

    Sprint delivery with unit, integration and contract tests.

  4. 50da3fa

    feat: performance and security checks

    Load tests, dependency scans and security review before release.

  5. 69d460f

    merge: release and operate

    CI/CD deployment, monitoring dashboards and an agreed support model.

What teams build with Java

  • Integration layer for core banking

    A bank exposes its core banking system through a Spring Boot integration layer with secure APIs, transformation logic and audit logs, so new mobile and partner channels connect without touching the core itself.

  • High-volume transaction processing

    A payments company processes large daily transaction volumes in Java services with idempotent operations, careful database tuning and horizontal scaling, keeping latency predictable and failures recoverable during festival and month-end peaks.

  • Modernizing a Java 8 monolith

    An aging Java 8 application is upgraded to a current long-term support release and Spring Boot, with tests added first, deprecated libraries replaced and the codebase reorganized into clear modules for future extraction.

  • Event-driven order processing with Kafka

    Orders flow through Kafka topics consumed by Java services for payment, inventory, shipping and notifications, each scaling independently and replaying events after failures without losing or duplicating a single customer order.

  • Insurance rating engine

    An insurer moves premium calculations into a Java rating service with versioned rules, tested against thousands of historical quotes, so new products and rate changes go live quickly without errors in pricing.

Where Java sits in your stack

The tools we pair it with, layer by layer. Select a layer to see what it is responsible for.

Technologies often used with Java

All 32 technologies

More in Backend

Java development FAQs

Something else on your mind? Ask a consultant and get a reply within one business day.

Why choose Java over Node.js or Python?

Java suits complex domains, high transaction volumes and long-lived enterprise systems that benefit from strong typing and JVM performance. Node.js and Python are often quicker for small APIs and MVPs. We recommend based on workload and team.

Do we need microservices?

Not always. A well-structured modular monolith is simpler to run and enough for many products. We introduce microservices when separate scaling, release cycles or team ownership justify the extra operational work.

What affects Java development cost?

System scope, integrations, data volumes, compliance requirements, migration complexity and testing depth drive effort. A fixed quote follows a free consultation.

Can you upgrade us from Java 8?

Yes. We upgrade through LTS versions, replace removed APIs and incompatible libraries, and run regression tests at each step.

How do you secure Java applications?

We use Spring Security, encrypted secrets, input validation, dependency vulnerability scanning and audit logging. NDAs are available on request.

What are Java virtual threads?

Virtual threads, a standard feature since Java 21 and further improved by Java 25, the current long-term support release, are lightweight threads managed by the JVM rather than the operating system. They let services handle very large numbers of concurrent blocking operations, such as database and HTTP calls, using simple synchronous code instead of complex asynchronous frameworks.

Should we start with a modular monolith instead of microservices?

For most new systems, yes. A modular monolith keeps deployment and operations simple while enforcing clean boundaries between domains. When a module genuinely needs independent scaling or a separate team, it can be extracted into a service, avoiding distributed complexity before it pays off.

We work with clients across the USA, UK, Australia, UAE, New Zealand and India.

Where we work

Tell us what you're building.

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