A practical guide to containerizing Spring Boot applications and deploying them to Kubernetes. Covers multi-stage Dockerfiles, Jib for zero-Docker builds, Kubernetes manifests, health probes, ConfigMaps, Secrets, resource limits, and production readiness checklist.

Maximize your local development speed with Spring Boot. Covers DevTools live reload, Docker Compose integration (auto-start containers), Testcontainers at development time, application-local.yml profiles, and IDE shortcuts that save hours.

See classic design patterns alive in Spring Boot. Covers Strategy (multiple payment processors), Template Method (abstract batch steps), Observer (ApplicationEvent), Decorator (filter chains), and Factory (bean selection) — with real code you can use today.

Implement CQRS and Event Sourcing in Spring Boot. Separate command and query models, store events as the source of truth, build projections for reads, and understand when this complexity is justified.

Master caching in Spring Boot with Redis and Caffeine. Covers @Cacheable, @CacheEvict, @CachePut, TTL strategies, multi-level caching (Caffeine L1 + Redis L2), cache-aside pattern, and production pitfalls.

Deploy Spring Boot to AWS three ways: as a serverless function on Lambda (with SnapStart), as containers on ECS Fargate, and with managed database on RDS. Covers cold start optimization, cost comparison, and when to use which approach.

Compare API versioning strategies in Spring Boot: URI path versioning, custom header versioning, and content negotiation. Covers implementation patterns, deprecation workflow, backward compatibility, and when to use which approach.

Secure your Spring Boot APIs against the OWASP Top 10. Covers injection prevention, broken auth, CORS, rate limiting, input validation, security headers, sensitive data exposure, and a production security checklist — with code for each vulnerability.

Build production-grade batch jobs with Spring Batch. Covers chunk-oriented processing, readers/writers, ItemProcessor, skip/retry, partitioning for parallel execution, scheduling, and monitoring.

Learn how to combine Spring AI tool calling with conversation memory so the model remembers previous tool results across turns. Build a stateful payment assistant that maintains context throughout a multi-turn conversation.