Hire Java Developers

Spring Boot is the modern standard. Java EE (now Jakarta EE) is for legacy maintenance.

Use Spring Boot for:

• New projects: Modern, opinionated, rapid development
• Microservices: Built-in patterns and tooling
• Cloud-native: Easy deployment to containers/cloud
• Developer experience: Auto-configuration, sensible defaults
• Ecosystem: Massive community, active development
• Spring Cloud: Microservices patterns (config, discovery, gateway)

Legacy Java EE when:

• Maintaining existing applications
• Organization standardized on Jakarta EE
• Specific enterprise requirements (rare)

Spring Boot advantages:

• Embedded server: No need for Tomcat/WildFly deployment
• Auto-configuration: Less boilerplate, faster development
• Production-ready: Actuator for health checks, metrics
• Testing: Excellent testing support
• Modern Java: Leverages latest Java features

Real-world usage:

• Spring Boot: Netflix, Amazon, Alibaba, eBay (millions of apps)
• Jakarta EE: Legacy systems, slower adoption

Our recommendation:

• All new Java projects should use Spring Boot
• Migrate legacy apps to Spring Boot when practical

Our Java developers are Spring Boot experts.

Both are excellent enterprise choices. The decision depends on team and requirements.

Choose Java for:

• Enterprise ecosystem: Mature frameworks, libraries, patterns
• Large teams: Better for complex, long-lived codebases
• Complex business logic: OOP patterns, extensive libraries
• Hiring: Largest developer pool worldwide
• Existing Java stack: Leverage team expertise
• Regulated industries: Financial, healthcare (mature ecosystem)

Choose Go for:

• Performance: 2-3x faster, lower latency
• Microservices: Faster startup, smaller footprint
• Cloud-native: Modern infrastructure tools (Kubernetes, Docker)
• Simplicity: Easier to maintain at scale
• Resource efficiency: Uses less memory, cheaper cloud costs

Performance comparison:

• Go: Faster execution, lower memory, faster startup
• Java: Good performance with tuning, heavier runtime (JVM)

Developer experience:

• Java: Larger talent pool, mature tooling, more verbose
• Go: Simpler language, faster compilation, growing adoption

Real-world usage:

• Java: LinkedIn, Twitter, Netflix backend, Amazon, eBay
• Go: Uber microservices, Dropbox infrastructure, Docker, Kubernetes

Our recommendation:

• Choose Java for enterprise apps, complex business logic, large teams
• Choose Go for performance-critical microservices, cloud-native systems

We have senior developers in both ecosystems.

Java 17 LTS or higher is recommended for new projects in 2025.

Java version landscape:

• Java 8: EOL for public updates (2019), legacy only
• Java 11 LTS: Still supported but aging (2018)
• Java 17 LTS: Minimum recommended (supported until 2029)
• Java 21 LTS: Latest LTS (supported until 2031)
• Java 22+: Non-LTS releases (6-month cycle)

Key features by version:

• Java 11: var keyword, HTTP client
• Java 17: Records, sealed classes, pattern matching (preview)
• Java 21: Virtual threads, sequenced collections, pattern matching

LTS vs non-LTS:

• LTS (17, 21): Long-term support (8+ years)
• Non-LTS: Feature releases (6-month support)
• Our recommendation: Use LTS for production

Why Java 17+ matters:

• Spring Boot 3.x: Requires Java 17 minimum
• Modern features: Records, text blocks, pattern matching
• Performance: Continuous GC and runtime improvements
• Security: Latest security patches

Migration considerations:

• Java has strong backward compatibility
• Most libraries support Java 17+
• Spring Boot 3.x requires Java 17+
• Cloud platforms support all recent versions

Our recommendation:

• New projects: Java 17 or Java 21 LTS
• Legacy projects: Plan migration to Java 17+

Our Java developers write modern Java leveraging recent language features.

Depends on use case. Reactive is powerful but adds complexity.

Use reactive (Spring WebFlux, Project Reactor) when:

• High concurrency: Thousands of simultaneous connections
• I/O-bound operations: External API calls, database queries
• Backpressure needed: Handle slow consumers
• Streaming data: Real-time event processing
• Resource efficiency: Better thread utilization

Use traditional (Spring MVC) when:

• Simple CRUD applications: Reactive adds unnecessary complexity
• Blocking operations: JDBC, certain libraries
• Team unfamiliarity: Reactive has steep learning curve
• Legacy integrations: Many libraries are blocking

Reactive advantages:

• Scalability: Handle more requests with fewer threads
• Backpressure: Flow control for data streams
• Composition: Chain async operations elegantly

Reactive challenges:

• Learning curve: Steep for teams new to reactive
• Debugging: Harder than traditional stack traces
• Ecosystem: Some libraries still blocking-only
• Complexity: Overkill for simple apps

Spring WebFlux vs Spring MVC:

• WebFlux: Reactive, non-blocking, Netty-based
• MVC: Traditional, blocking, Tomcat-based
• Both: Can coexist in same app

Common reactive patterns:

• Mono (0 or 1 item)
• Flux (0 to N items)
• Operators (map, flatMap, filter)
• Backpressure handling

Our recommendation:

• Start with Spring MVC for most applications
• Use WebFlux when you have proven performance needs
• Don’t use reactive just because it’s trendy

Our Java developers are proficient in both traditional and reactive Spring.

Absolutely. Java, particularly Spring Boot, excels at microservices.

Spring Boot microservices advantages:

• Spring Cloud: Built-in microservices patterns
• Mature ecosystem: Circuit breakers, config, discovery
• Production-ready: Actuator for health, metrics, monitoring
• Developer productivity: Auto-configuration, starters
• Enterprise-grade: Battle-tested at massive scale

Spring Cloud stack:

• Spring Cloud Gateway: API gateway, routing
• Eureka / Consul: Service discovery
• Config Server: Centralized configuration
• Circuit Breaker (Resilience4j): Fault tolerance
• Sleuth + Zipkin: Distributed tracing
• Spring Cloud Stream: Event-driven messaging

Java microservices patterns:

• Service discovery and registration
• API gateway pattern
• Circuit breakers for resilience
• Distributed tracing
• Centralized logging (ELK stack)
• Event-driven communication (Kafka)

Microservices deployment:

• Docker containers
• Kubernetes orchestration
• Service mesh (Istio, Linkerd)
• Observability (Prometheus, Grafana)

When to use microservices:

• Large teams needing independent deployment
• Different scaling requirements per service
• Complex domains (DDD benefits)
• Polyglot architecture needs

When NOT to use microservices:

• Small team (< 10 developers)
• MVP or early-stage product
• Monolith is working fine
• Operational complexity outweighs benefits

Our recommendation: Start with a well-architected modular monolith. Extract microservices only when clear scaling or organizational needs emerge.

Our Java developers can build both monoliths and microservices.

Yes. JVM tuning is critical for high-performance applications.

JVM optimization areas:

Garbage Collection (GC) tuning:

• G1GC: Default, balanced (Java 9+)
• ZGC: Low-latency (sub-10ms pauses)
• Shenandoah: Low-latency alternative
• Parallel GC: High throughput
• Tune heap size (-Xms, -Xmx)
• Monitor GC logs

Memory management:

• Heap sizing (young gen, old gen)
• Metaspace configuration
• Off-heap memory
• Memory leak detection
• Heap dump analysis

JVM flags:

• Enable GC logging
• Performance tuning flags
• Diagnostic flags
• Container-aware settings

Profiling tools:

• JProfiler: Commercial profiler
• VisualVM: Free profiler
• async-profiler: Low-overhead profiling
• JFR (Java Flight Recorder): Built-in profiling
• APM tools: New Relic, Datadog, Dynatrace

Performance optimization:

• Avoid premature optimization
• Profile first (CPU, memory, allocations)
• Optimize hot paths
• Connection pooling
• Caching strategies (Redis, Caffeine)

Common bottlenecks:

• Excessive object allocation
• GC pressure (too many short-lived objects)
• Database queries (N+1 problem)
• Inefficient algorithms
• Synchronization overhead

Container optimization:

• Set -XX:MaxRAMPercentage for containers
• CPU and memory limits
• JVM aware of cgroups (Java 10+)

Our recommendation: Profile before optimizing. Modern JVM is well-tuned out-of-the-box for most applications.

Our Java developers optimize JVM for production performance.

Testing is fundamental to professional Java development.

Testing practices:

• JUnit 5: Modern testing framework
• Mockito: Mocking dependencies
• AssertJ: Fluent assertions
• Spring Boot Test: Integration testing
• TestContainers: Database/service testing with Docker

Test types:

• Unit tests: Individual classes, methods
• Integration tests: Multiple components, database
• E2E tests: Full application flow
• Contract tests: API contracts (Spring Cloud Contract)

Test patterns:

• Arrange-Act-Assert (AAA)
• Test fixtures with @BeforeEach
• Parameterized tests (JUnit 5)
• Test slices (Spring Boot: @WebMvcTest, @DataJpaTest)

Code quality tools:

• SonarQube: Code quality & security analysis
• Checkstyle: Code style enforcement
• PMD: Code analysis
• SpotBugs: Bug detection
• JaCoCo: Code coverage (aim for 80%+)

CI/CD practices:

• Automated tests on every PR
• Code coverage reporting
• Static analysis in pipeline
• Security scanning (OWASP Dependency-Check)
• Build verification

Spring Boot testing:

• @SpringBootTest: Full integration tests
• @WebMvcTest: Controller tests
• @DataJpaTest: Repository tests
• @MockBean: Mock Spring beans
• TestRestTemplate: REST API testing

Our recommendation: Test pyramid: Many unit tests, fewer integration tests, minimal E2E tests.

Our Java developers write comprehensive tests for reliable code.

Absolutely. Security is critical for enterprise Java applications.

Spring Security features:

• Authentication: Username/password, OAuth2, SAML, LDAP
• Authorization: Role-based access control (RBAC)
• JWT tokens: Stateless authentication
• OAuth2: Integration with identity providers
• CSRF protection: Built-in CSRF tokens
• Password encoding: BCrypt, Argon2

Security best practices:

• Never store plain-text passwords
• Use parameterized queries (prevent SQL injection)
• Validate all input
• Use HTTPS (TLS)
• Implement rate limiting
• Security headers (CORS, CSP, X-Frame-Options)

Authentication patterns:

• Session-based (traditional web apps)
• JWT tokens (stateless APIs, microservices)
• OAuth2 (third-party integration)
• Multi-factor authentication (MFA)

Authorization:

• Method-level security (@PreAuthorize, @Secured)
• URL-based security (antMatchers)
• Expression-based (SpEL)
• Custom voters

Common vulnerabilities (OWASP Top 10):

• SQL Injection (use JPA/prepared statements)
• XSS (escape output)
• CSRF (Spring Security handles this)
• Insecure dependencies (keep updated)
• Authentication flaws (use Spring Security)

Security scanning:

• OWASP Dependency-Check
• Snyk (dependency scanning)
• SonarQube security rules
• Penetration testing

Compliance:

• GDPR, HIPAA, PCI-DSS support
• Audit logging
• Data encryption (at rest, in transit)

Our Java developers implement enterprise-grade security.

Kotlin is valuable but not essential. Java remains the primary language.

Why Kotlin is popular:

• Null safety: Eliminates NullPointerExceptions
• Concise syntax: Less boilerplate than Java
• Interoperability: 100% Java interop
• Spring support: First-class Spring Boot support
• Coroutines: Built-in async/concurrency

When Kotlin makes sense:

• Android development (Kotlin is preferred)
• New Spring Boot projects (developer preference)
• Teams want modern syntax
• Null safety is critical

When Java is sufficient:

• Existing Java codebases
• Team expertise in Java
• Enterprise standards require Java
• Legacy systems integration

Java vs Kotlin:

• Java: Larger talent pool, more mature, universal
• Kotlin: More concise, null-safe, modern features
• Both: Interoperable, can use together

Spring Boot + Kotlin:

• Kotlin is well-supported in Spring
• Many companies mixing Java and Kotlin
• Incremental adoption possible

Our recommendation:

• Java primary: Master Java first
• Kotlin secondary: Learn if team/project benefits
• Both acceptable: Spring Boot works great with both

Our Java developers are proficient in Java; many also know Kotlin.

Yes. Java integrates well with all major cloud platforms.

AWS deployment:

• Elastic Beanstalk: PaaS for Spring Boot
• ECS / EKS: Container orchestration
• Lambda: Serverless Java (with GraalVM for fast cold starts)
• RDS: Managed PostgreSQL/MySQL
• ElastiCache: Managed Redis
• SDK for Java: AWS service integration

GCP deployment:

• App Engine: PaaS for Java
• Cloud Run: Containerized apps
• GKE: Kubernetes
• Cloud SQL: Managed databases
• Java client libraries: GCP integration

Azure deployment:

• App Service: PaaS
• AKS: Kubernetes
• Spring Cloud Azure: Azure integration
• Azure SQL Database

Cloud-native patterns:

• 12-factor app principles
• Externalized configuration (Spring Cloud Config)
• Health checks (Actuator)
• Graceful shutdown
• Horizontal scaling

Containerization:

• Docker with multi-stage builds
• Optimized base images (Eclipse Temurin, Adoptium)
• JVM flags for containers
• Kubernetes deployment manifests

Serverless Java:

• AWS Lambda (Java 17+)
• GraalVM native image (faster cold starts)
• Micronaut / Quarkus (optimized for serverless)

Our recommendation: Spring Boot deploys easily to any cloud. Start with managed services (ECS, Cloud Run) before Kubernetes complexity.

Our Java developers deploy and manage cloud-based applications.

Yes. Legacy modernization is a common and valuable skill.

Common legacy scenarios:

• Java 8 → Java 17+ migration
• Monolith → microservices
• Java EE → Spring Boot
• On-premise → cloud
• Manual deployment → CI/CD

Modernization strategies:

1. Strangler Fig Pattern:

• Build new features in modern stack
• Gradually migrate old features
• Eventually replace legacy entirely

2. Branch by Abstraction:

• Abstract old code behind interfaces
• Implement new version
• Switch over when ready

3. Big Bang Rewrite:

• Rare and risky
• Only for small systems
• Usually not recommended

Migration approaches:

• Java version upgrade: Low risk, high value
• Framework migration: Medium risk, moderate value
• Architecture change: High risk, high value
• Cloud migration: Medium risk, variable value

Challenges:

• Lack of tests (write tests first!)
• Undocumented behavior
• Tight coupling
• Technical debt
• Risky deployments

Best practices:

• Add tests before refactoring
• Incremental changes
• Feature flags for rollback
• Monitor closely
• Keep old system running

Tools:

• OpenRewrite (automated refactoring)
• Spring Boot Migrator
• Static analysis (SonarQube)
• Dependency management (Maven/Gradle)

Our recommendation: Incremental modernization is safer and more successful than big bang rewrites.

Our Java developers specialize in legacy modernization.

Yes. Solo developers, pairs, or full cross-functional teams.

Option 1: Solo Java Developer

• Perfect for adding backend capacity
• Integrates with existing team

Option 2: Java Team

• 2-3 Java/Spring Boot Developers
• Full backend development capacity

Option 3: Full Product Team

• 2-3 Java Developers
• 1-2 Frontend Engineers (React, Angular)
• 1 UI/UX Designer
• 1 QA Engineer
• 1 Product Manager (optional)

All managed by Ideaware for HR, payroll, and retention.