Spring Boot is powerful. But knowing the framework isn’t the same as understanding backend engineering. In this video, I walk through the roadmap I believe matters for a Spring developer in 2026. We start with data. That means real SQL — CTEs, window functions, normalization trade-offs — and understanding what ACID and BASE actually imply for system guarantees. Spring Data JPA is useful, but you still need to know what happens underneath. Then architecture: microservices vs modular monolith, serverless, CQRS, and when HTTP, gRPC, Kafka, or WebSockets make sense. Not as buzzwords — but as design choices with trade-offs. Security and infrastructure follow: OWASP Top 10, AuthN vs AuthZ, encryption in transit and at rest, Docker, Kubernetes, Infrastructure as Code, and observability with Micrometer, OpenTelemetry, and Grafana. This roadmap isn’t about mastering every tool. It’s about knowing what affects reliability in production.
Most Java AI demos stop at prompt loops. That doesn't scale in production. In this video, we integrate Embabel into an existing Spring Boot application and build a multi-step, goal-driven agent for incident triage. Instead of manually orchestrating prompt → tool → prompt cycles, we define typed actions and let the agent plan across deterministic and LLM-powered steps. We parse structured input with Ollama, query MongoDB deterministically, classify risk using explicit thresholds, rank affected implants, generate a constrained root cause hypothesis, and produce a bounded containment plan. LLM handles reasoning. Java enforces rules. This is about controlled AI workflows on the JVM — not prompt glue code.
Spring Data MongoDB breaks down fast once CRUD meets production—real queries, actual data volumes, analytics. What looks simple at first quickly turns into unreadable repository methods, overfetching, and slow queries. In this video, I walk through building a production-style Spring Boot application using Spring Data MongoDB — starting with basic setup and repositories, then moving into indexing, projections, custom queries, and aggregation pipelines. You'll see how MongoDB's document model changes data design compared to SQL, when embedding helps, and when it becomes a liability. We cover where repository method naming stops scaling, how to use @Query safely, when to switch to MongoTemplate, and how to reduce payload size with projections and DTOs. Finally, we implement real MongoDB aggregations to calculate analytics directly in the database and test everything against a real MongoDB instance using Testcontainers. This is not another MongoDB overview. It's a practical guide to actually using Spring Data MongoDB in production without fighting the database.


