JRush | Container Essentials: Fast Builds, Secure Images, Zero Vulnerabilities
Summary
This session of JRush focuses on modern application security from multiple angles: containerization, developer tooling, and secure deployment practices. Speakers discuss cloud native buildpacks as a safer alternative to handwritten Dockerfiles, showing how they simplify builds, improve reproducibility, and reduce security risks. The talk also demonstrates how IntelliJ IDEA and JetBrains security tooling help developers detect vulnerabilities early through inspections, taint analysis, and CI/CD integration. Finally, hardened container images are presented as a new security baseline that minimizes attack surface, reduces CVEs, and simplifies compliance. Overall, the episode emphasizes shifting security left through better tooling, standardized images, and shared responsibility across teams.
About Catherine
Java developer passionate about Spring Boot. Writer. Developer Advocate at BellSoft
Social Media
Tags
Videos
Feb 6, 2026
Backend Developer Roadmap 2026: What You Need to Know
Backend complexity keeps growing, and frameworks can't keep up. In 2026, knowing React or Django isn't enough. You need fundamentals that hold up when systems break, traffic spikes, or your architecture gets rewritten for the third time.I've been building production systems for 15 years. This roadmap covers three areas that separate people who know frameworks from people who can actually architect backend systems: data, architecture, and infrastructure. This is about how to think, not what tools to install.
Videos
Jan 29, 2026
JDBC Connection Pools in Microservices. Why They Break Down (and What to Do Instead)
In this livestream, Catherine is joined by Rogerio Robetti, the founder of Open J Proxy, to discuss why traditional JDBC connection pools break down when teams migrate to microservices, and what is a more efficient and reliable approach to organizing database access with microservice architecture.
Further watching
Videos
Feb 27, 2026
Spring Developer Roadmap 2026: What You Need to Know
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.
Videos
Feb 18, 2026
Build Typed AI Agents in Java with Embabel
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.
Videos
Feb 12, 2026
Spring Data MongoDB: From Repositories to Aggregations
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.