Java Development for Systems That Have to Stay Up
Spring Boot microservices, high-throughput APIs and the careful modernization of Java systems that have been running for a decade, built by engineers who have maintained code they did not write.
What do Java development services cover?
Java development services cover back-end engineering on the JVM: Spring Boot services and REST or GraphQL APIs, message-driven and event-based architectures, database and persistence design, performance tuning, and the upgrade or re-platforming of older Java and Java EE systems. Anviam builds new Spring Boot services and also takes over existing Java codebases, including applications on Java 8 or Java EE that need a supported runtime without a rewrite.
- Java versions
- Java 8 through 21 LTS
- Core framework
- Spring Boot & Spring Cloud
- Architectures
- Monolith, modular, microservices, event-driven
- Common brief
- New services or legacy JVM upgrades
- Testing
- JUnit, Testcontainers, contract tests
- Engagement
- Project team or dedicated Java pod
Java Is Chosen for the Boring Reasons, and They Are Good Reasons
Java keeps winning back-end decisions in banking, insurance, logistics and healthcare because of properties that do not demo well: a mature concurrency model, predictable performance under sustained load, excellent observability tooling, and a hiring pool that will still exist in ten years. When a system moves money or schedules clinical care, those matter more than novelty.
The flip side is that a lot of production Java is old. We spend as much time on Java 8 applications that need a supported runtime, or Java EE monoliths that need decomposing, as on greenfield Spring Boot work. Both need the same discipline: understand the current behaviour, get tests around it, then change it in steps you can reverse.
Java Development Services We Provide
Spring Boot Development
Production Spring Boot services with sensible configuration management, health checks and observability built in.
REST & GraphQL API Development
Versioned, documented APIs with contract tests, so consumers are not broken by a deploy.
Microservices Architecture
Service decomposition along real business boundaries, with the operational tooling to run more than one service.
Event-Driven Systems
Kafka and RabbitMQ pipelines with idempotent consumers and replay strategies for when things fail.
Java & JVM Upgrades
Moving Java 8 and Java EE applications onto supported LTS runtimes, dependency by dependency.
Performance & JVM Tuning
Profiling, garbage-collection tuning and query optimisation for systems hitting throughput or latency ceilings.
The Java Ecosystem We Work In
Typical Java Engagements
Core Banking Integrations
Services that talk to core banking and payment rails where correctness beats throughput.
Insurance Policy Systems
Rating, quoting and policy administration logic with heavy rule sets and audit needs.
Logistics & Supply Chain
High-volume order, routing and tracking services with strict latency budgets.
Healthcare Interoperability
HL7 and FHIR integration services moving clinical data between systems safely.
Java EE Decomposition
Breaking WebLogic and JBoss monoliths into deployable services without a freeze on feature work.
Runtime Modernization
Getting off end-of-life Java versions to remove security exposure and unblock hiring.
How We Approach a Java Codebase
Assess & Instrument
Static analysis, dependency audit and runtime profiling to establish what the system really does.
Establish a Test Baseline
Characterisation tests around current behaviour before changing anything, so regressions are visible.
Upgrade or Extract in Steps
Incremental dependency upgrades or service extraction, each independently deployable and reversible.
Automate the Pipeline
Build, test, security scan and deploy automated, so releases stop being events.
Hand Over or Continue
Documentation and knowledge transfer, or an embedded Java pod that keeps delivering.
Common Questions About Java Development
Usually yes. Most Java 8 to 17 or 21 migrations are a dependency problem rather than a language problem: the code compiles with modest changes, but libraries, build plugins and anything touching removed JDK internals need attention. We audit dependencies first, produce a migration order, then upgrade incrementally with tests at each step. Applications relying heavily on Java EE APIs removed from the JDK need more work, and we scope that separately.
Only if the monolith is actually the problem. Microservices trade code complexity for operational complexity, and they pay off when independent teams need to deploy independently, or when parts of the system have very different scaling profiles. If deploys are slow because tests are slow, or changes are risky because coverage is thin, microservices will make both worse. We assess honestly, and modular monoliths are a frequent recommendation.
Yes. A significant share of our Java work is on applications running on WebLogic, JBoss or WebSphere, often with EJBs and vendor-specific configuration. The usual path is to isolate business logic from container-specific APIs, get it under test, and move it onto Spring Boot service by service while the original application keeps serving traffic.
Yes. Alongside project delivery, you can engage Java engineers as a dedicated pod that works in your repositories, your sprint cadence and your ticketing system, reporting to your tech lead. Typical onboarding is five to ten business days, with at least four hours of daily overlap with US, UK or Australian working hours.
By measuring before changing. We profile under realistic load to find where time actually goes, which is usually database access patterns, N+1 queries, lock contention or serialisation rather than the code people suspect. Garbage-collection tuning matters, but it is rarely the first win. You get a written findings report with measured before-and-after numbers, not a list of generic best practices.
Unit tests on business logic, integration tests against real dependencies using Testcontainers rather than mocks that drift from reality, and contract tests between services so a producer change cannot silently break a consumer. On legacy work we add characterisation tests first, capturing what the system does today even where that behaviour is odd, because that is what users depend on.