JRush Episode 4: Q&A Session

Transcript:

Q&A Session Introduction

I would prefer to call this a panel discussion rather than a simple Q&A session. We likely have a number of questions from our audience, which I would like to voice to our speakers now. Additionally, I have a "secret" question for every speaker at this panel, which, of course, is about Java.


Question for Dmitry: ARM Servers Availability

Dmitry, you mentioned that ARM servers are available in the cloud at AWS. Do we know of any other cloud providers or manufacturers offering ARM servers where they can be accessed or purchased?

  • Dmitry: Yes, I mentioned that at the end of the talk. You can rent servers built on Hunter Ultra in Oracle Cloud or Azure, or you can buy physical servers from multiple vendors based on Ultra. Graviton servers, however, are available only on AWS EC2. There are other providers as well, such as Alibaba Cloud.
  • Host: Interesting! I found that instances on ARM servers like AWS Gravitons are really cheaper than Intel ones, so it’s definitely worth checking.

Question for Mary: Streaming Technologies

Mary, we know of several different streaming technologies like Apache Spark, Apache Flink, and Apache Pulsar. Could you briefly explain the differences between them?

  • Mary: Sure! Apache Pulsar is more of an event-streaming broker, ideal for message ingestion. Apache Spark is more suited for batch processing of streaming data, often used in machine learning and AI applications. Apache Flink allows both ingestion and event-streaming processing, making it great for complex SQL-based data operations. Together, they form a strong combination, leveraging their individual strengths.
  • Host: That’s great. Can this technology help with fraud detection?
  • Mary: Yes, definitely. For instance, Apache Pulsar can monitor data in real time to detect unusual activities in financial systems, helping prevent fraud. Event streaming software can be highly effective for such purposes.


Technical Question: Garbage Collection in Pulsar

There’s a concern about garbage collection causing "stop-the-world" events in Pulsar. How does it handle this issue?

  • Mary: That’s a great question. Currently, I don’t have the exact answer but can look it up. However, Pulsar’s federated architecture, which uses multiple nodes, minimizes the likelihood of stop-the-world pauses affecting the entire system.

Question for Dr Mo: Kubernetes in Production

Dr Mo, could you share your experience using Kubernetes or OpenShift in production?

  • Dr Mo: Sure. At Discover, we use OpenShift, which builds on Kubernetes with added features for developers and operation engineers. OpenShift simplifies deployment, scalability, and management through an intuitive console and CI/CD integration.
  • Host: Is transferring solutions across cloud providers easy with OpenShift?
  • Dr Mo: Yes, with tools like Red Hat’s Advanced Cluster Management, you can manage applications across clouds easily. It’s more seamless with OpenShift compared to vanilla Kubernetes, though complex applications might require additional work.

Final Question: Java’s Longevity

Java recently celebrated its 28th anniversary. What do you think makes Java so popular after all these years?

  • Mary: The vibrant community and robust technology keep Java alive. The Java Community Process (JCP) and the absence of major internal disputes make it unique. Java’s reliability in production also speaks volumes.
  • Dr Mo: Java simplifies development by abstracting complexities like memory management. Its active development, backward compatibility, and community support make it indispensable in industries like finance.
  • Dmitry: Java’s strict backward compatibility and a strong ecosystem have fostered trust and continuous development over decades.

Summary

At the end of JRush Episode 4 “Modern Java development for Banking and FinTech”, we had a very fruitful discussion with our speakers, Dr Mo Haghighi, Mary Grygleski, and Dmitry Chuyko, who answered the questions from the audience, such as Where can we get access to Arm servers? Can Apache Pulsar be used for fraud detection in banking? How to use Kubernetes in production? And more!

About Dmitry

Dmitry Chuyko is a Senior Performance Architect at BellSoft, an OpenJDK committer, and a public speaker. Prior to joining BellSoft, Dmitry worked on the Hotspot JVM at Oracle, and before that he had many years of programming experience in Java. He is currently focused on optimizing HotSpot for x86 and ARM, previously being involved in rolling out JEP 386, which enables the creation of the smallest JDK containers.

Social Media

Videos
card image
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
card image
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
card image
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
card image
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
card image
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.