My 2 Cents: I'll gladly spend them to stop staring at test logs

Most GraphQL Gateway and general GraphQL discussions focus on customer-facing applications—aggregating microservices behind a single endpoint for web and mobile clients, enabling frontend teams to query exactly the data they need without multiple round trips. Our use case and perspective were different.

The output of most automated accessibility tests is a long list of violations. This format, while comprehensive, makes it difficult to distinguish new issues introduced in a feature branch from long-standing technical debt. It doesn’t clearly show if accessibility is improving, and it doesn't help prevent regressions.

Kafka sits at the heart of how we move data between systems at trivago. Many teams publish changes to Kafka, and downstream services consume those changes to keep user-facing features up to date—things like accommodation reviews, highlights, and other derived attributes.

About six years ago, our mobile automation setup was showing its age. It was a small, homegrown framework that had worked “well enough” for a long time, until we tried to upgrade Java and a few core dependencies. That’s when things started breaking in ways we couldn’t reasonably patch around. The framework simply wasn’t built to move forward.

It’s a scenario developers relate to a little too well. The product manager always comes with more and more feature requests. They also want to release fast by giving a tight deadline. While you, as a developer, care more about stability, performance, and maintainability of your application.

This tension can easily turn into friction. But what if there was a way to align both sides; using data instead of relying on opinions? We are betting on a practice called Service Level Objectives (SLO). Can it stop the fight? 🤔

Mob programming often sparks hot takes. “It’s counter‑productive.” “One person could do it faster.” “Why waste five people on one keyboard?” If that’s what it looks like in your head—one senior typing while others half‑listen—that wasn’t mob programming. Real mob programming is structured collaboration that turns a team’s combined attention into forward motion.

Between 2023 and 2025, our approach to artificial intelligence shifted from isolated experimentation to company-wide integration. This transformation has delivered measurable productivity gains, accelerated innovation, and fostered a culture of knowledge sharing. As our AI Ambassador group celebrates its two-year anniversary in October 2025, we reflect on the data, the challenges, and the strategic direction for AI at trivago.

When doing exploratory testing on a web application, the nature of activities involves discovering things and not repeating the same patterns. However, there might be aspects that could be improved by specific tools without trespassing into the realm of test automation. Can coding skills help testing activities, beyond the somewhat obvious case of automated tests? In the trivago QA team we asked ourselves this question, trying to find solutions to make some testing processes less cumbersome and save time while maximizing focus.

If you are a Developer or a Product person, you might have this feeling of achievement when you work on a specific product. When it’s launched successfully in the market, you see people start using something built by you. You become proud because your hard work results in something that is helping many people to achieve their tasks. This is a rewarding feeling for many of us!

Price comparison is one of the key value propositions of trivago, and in doing so, we handle billions of prices everyday to provide our users with the most accurate and up to date price as possible. We can leverage this enormous amount of historic price data to provide price context for our users in a scalable and fast way.