Amsterdam Java User Group

Amsterdam JUG OTel Workshop at Dash0

30 sep 2026 18:00 CEST

Nieuwezijds Voorburgwal 162, Nieuwezijds Voorburgwal 162, 1012 SJ Amsterdam, Netherlands, Amsterdam, NH, NL

Join in with the latest Amsterdam JUG Workshop at Dash0 (which is at Dash0, Nieuwezijds Voorburgwal 162/Suite 401, 1012SJ Amsterdam), in which we learn all the basic principles of OpenTelemetry, while eating pizza, (and celebrating Yoeri Krom's birthday).

Important:

• Bring your own laptop because this is a hands-on workshop where you'll be learning about OpenTelemetry from scratch!
• On your laptop, you need a local Kubernetes cluster to already be running (e.g., kind or k3s).
• On your laptop, you need to have a Java, Node, .NET, Python or Ruby application that is important to you and that you know how to deploy on Kubernetes; if you don’t have one, we will have test applications available, but that would only be half the fun!
• Limited seats, exclusive workshop, sign up quick!

Agenda

17:30 — Doors Open (and Food and Drinks)
18:00–18:20 — OpenTelemetry for Complete Beginners (Part 1).
18:20–18:50 — OpenTelemetry for Complete Beginners (Part 2).
18:50–19:20 — Hands on Lab 1: Instrumenting a demo app.
19:20–19:35 — Break.
19:35–20:10 — Hands on Lab 2: From data to answers.
20:10–20:40 — Hands on Lab 3: Break things on purpose.
20:40–21:00 — Wrap-up.

Details on Labs

Lab 1: Instrument a demo app. Add OpenTelemetry to a small service from scratch: SDK, auto-instrumentation, and a collector. You'll send the resulting traces, metrics, and logs into a demo environment so you can see what your instrumentation actually produces.

Lab 2: From data to answers. With telemetry flowing, we'll work through real questions: which service is slow, why, and what changed. The environment that we set up in Lab 1 is our workbench, but the techniques (correlating traces with logs, reading resource attributes, querying by semantic conventions) apply to any OTel-compatible backend.

Lab 3: Break things on purpose. Introduce latency, errors, and a misbehaving dependency into the demo app, while tracing the failure back to its cause using only what OTel gives you. The goal is to see how much of a real incident you can reconstruct from the signals, using the demo environment to check whether your diagnosis continues to makes sense.

Amsterdam JUG Meetup at Mollie

05 okt 2026 18:00 CEST

Mollie, Keizersgracht 126, 1015 CW Amsterdam, Netherlands, Amsterdam, NH, NL

Join in with the latest Amsterdam JUG Meetup at Mollie, Keizersgracht 126, in central Amsterdam.

Agenda:

17:30: Doors open (and food)
18:00 - 18:45 - Talk 1: "Your Spring Boot Production is Bleeding. Let's Fix That." -- Mikhail Polivakha
18:45 - 19:30 - Talk 2: "Building Scalable, Low-Latency Fraud Detection AI in Java" -- Zoran Sevarac
19:30 - 20:15 - Talk 3: "Breaking the Monolith: Learnings from a Year of Improvements" -- Murat Özkan
20:15 - 21:00 - Talk 4: "HTTP: How Teenagers Talk to Their Parents" -- Hanno Embregts

Abstracts:

Talk 1: "Your Spring Boot Production is Bleeding. Let's Fix That." -- Mikhail Polivakha

The Java community shares a stack: Spring, Hibernate, Spring Data, Jackson. And with it a set of best practices we hear on repeat:

• Disable Open Session in View in production
• Avoid N+1, EAGER fetching, Cascade.REMOVE on collections
• Adopt Project Leyden and Lilliput and so on.

We know the rules by heart. But who checks that every service in the fleet actually follows them? In this talk, I will introduce Axelix, an OSS toolkit that closes that gap for Spring Boot ecosystems.

A lightweight starter plus a build-system plugin discovers each service's misconfigurations, persistence anti-patterns, and JVM improvement opportunities, then streams them to the Axelix Master - a single monitoring view, fleet-wide and per service.

We will go from a bare app to actionable insight live and see how Axelix exposes the opportunities to fix the findings to AI Agents via a built-in MCP server.

Talk 2: "Building Scalable, Low-Latency Fraud Detection AI in Java" -- Zoran Sevarac

For modern payment platforms, fraud detection is not only a machine learning problem — it is a business-critical capability. As transaction volumes grow, payment providers need to detect risk effectively while maintaining fast payment processing, controlling infrastructure costs, and scaling without adding unnecessary operational complexity.

In this session, we’ll show how an end-to-end fraud detection pipeline can be built entirely in Java — from transaction ingestion and feature processing to machine learning inference and fraud scoring.

Using payment fraud detection as a practical use case, we’ll examine the engineering decisions that directly affect business outcomes: how to increase transaction-processing capacity, maintain predictable low latency, reduce infrastructure overhead, simplify integration, and scale fraud detection cost-effectively as payment volumes grow.

Talk 3: "Breaking the Monolith: Learnings from a Year of Improvements" -- Murat Özkan

After a year of effort reducing the complexity of a 15-year-old monolithic codebase at Miro, we are back to share our learnings, both technical and
organizational. Organizational challenges often get overlooked, but they are critical for success at scale.

On the organizational side, we will discuss some "controversial" topics, such as the value of modularization, building and keeping sponsorship, prioritizing by impact, and the role of standards and observability. Without those, this project wouldn't have got past a personal hobby.

On the technical side, we will talk about some patterns that caused tight coupling in the codebase over the years, and how we detect and remove them without disrupting day-to-day development. That includes the role of agents in the trenches.

This talk is for any developer, team lead, or architect trying to improve a codebase that is too big to fix alone.

Talk 4: "HTTP: How Teenagers Talk to Their Parents" -- Hanno Embregts

As a parent, I’ve often found that getting a straight answer from a teenager can feel like an exercise in debugging. This realisation led me to discover the surprisingly close parallels between navigating those conversations and understanding HTTP status codes.

This talk won’t be about parenting (though there will be anecdotes!), but rather about decoding the language that modern systems use to communicate – success, failure, and everything in between.

We’ll explore common HTTP status codes, their implications for developers, testers, architects, and anyone who works with data, and discover how these codes can help us troubleshoot problems, build more resilient applications, and perhaps even understand the often-mysterious responses we receive from the teenagers in our lives.