Ensure microservices don't break each other with Spring Cloud Contract. Covers consumer-driven contracts, Groovy DSL, auto-generated tests, stub runner, and the workflow between producer and consumer teams.
Build real-time features with Spring Boot WebSocket and STOMP. Covers bidirectional communication, message broker, pub/sub topics, server-push notifications, and a working chat application.
A hands-on guide to integration testing Spring Boot applications with Testcontainers. Covers PostgreSQL, Kafka, Redis — real containers replacing mocks. Includes @ServiceConnection, dynamic properties, test slicing, and CI/CD setup with complete working examples.
Master job scheduling in Spring Boot. Covers @Scheduled (fixedRate, fixedDelay, cron), thread pool configuration, ShedLock for distributed environments, Quartz for complex workflows, and patterns to avoid duplicate execution across instances.
Solve the dual-write problem with the Transactional Outbox Pattern. Save domain events to an outbox table in the same DB transaction, then relay them to Kafka — guaranteeing at-least-once delivery without distributed transactions.
Build a complete observability stack for Spring Boot with OpenTelemetry, Grafana, Prometheus, Loki, and Tempo. Covers metrics collection, structured logging, distributed tracing, custom spans, correlation IDs, dashboards, and alerting — with Docker Compose and working code.
Build a multi-tenant Spring Boot application. Covers schema-per-tenant with AbstractRoutingDataSource, row-level filtering with Hibernate filters, tenant resolution from JWT/headers, and isolation strategies.
Build Spring Boot applications with MongoDB. Covers document modeling, MongoRepository, custom queries with @Query, aggregation pipelines with MongoTemplate, indexing, embedded vs referenced documents, and when to choose MongoDB over relational databases.
Production logging done right with Spring Boot. Covers structured JSON logging, correlation IDs with Micrometer Tracing, MDC context propagation, log levels strategy, Logback configuration, sensitive data masking, and log aggregation patterns.
Process data in real-time with Spring Boot and Kafka Streams. Covers stream topologies, KStream/KTable, filtering, transforming, windowed aggregations, state stores, and interactive queries — beyond simple producer/consumer.