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Meetups are one of our favourite events at trivago. They give us the opportunity to learn, share knowledge & experience, as well as network with fellow professionals and enthusiasts in the industry and community.

Greetings! It’s remarkable how technology continually evolves and impacts the tools we use for creating and developing products. Figma has recently gained recognition as an excellent alternative to Sketch and Abstract, offering product teams a more collaborative and efficient design process. At trivago, our design team consists of passionate Figma admirers. While we acknowledge that each organization has unique needs and procedures, we found switching from Sketch to Figma a valuable overall experience. Figma is filled with collaborative and user-friendly tools for ideation and prototyping that sets it apart from its competitors.

trivago provides travelers with an extensive collection of hotels, empowering them to compare prices and uncover the best vacation deals. With so many exceptional options available, we have introduced a new feature called "Favorites" to streamline the navigation process. This feature enables users to effortlessly save their preferred accommodations and access them later, ensuring ease of use. To access this feature, visit https://www.trivago.com/en-US/favorites.

Data validation is an essential step in any data processing pipeline, as it ensures the integrity and accuracy of the data to be used across all subsequent processing steps. Great Expectations (GX) is an open-source framework that provides a flexible and efficient way to perform data validation, allowing data scientists and analysts to quickly identify and correct any issues with their data. In this article, we share our experience implementing Great Expectations for data validation in our Hadoop environment, and our take on its benefits and limitations.

A tech conference is a gathering of tech enthusiasts, geeks, and wizards who come together to share their magic spells (aka tech knowledge), cast some illusions (aka demos), and talk about the future of technology in a professional yet humorous way. It's a place where you can explore the latest tech trends, make new connections, and have a great time with like-minded individuals. So, pack your wizard hat and prepare to be inspired by our tech conference called trivago Tech GetTogether (TGT)!

As a part of the series of posts already mentioned on WARP - A Web Application Rewrite Project, we are disclosing our process of making technical decisions. We hope that you find this process helpful. Maybe you can even pull something out for your own projects.

Over the last few years, we completely refactored what was described in our previous article about how we use the ELK stack for an overview of our test automation results, but some core concepts remain valid and applicable.

Hello, I am a product manager here at trivago. I have worked on different parts of the product such as apps, alternative accommodations, landing pages, and search & flow. We work in cross-functional teams that solve problems within a defined scope. My key responsibilities as a product manager are developing a product vision & strategy, defining objectives & outcomes, driving product discovery efforts such as user research, solution ideation, solution testing, and supporting engineering in product delivery. Overall it is about helping the team to ship the right product.

You have always been an engineer, solving problems and writing code. Now, there is an opportunity to become an engineering manager. You are interested.

However, questions arise.

While working with data and analyzing the interactions of our users with the products we have today, it is essential to understand their behaviors by tracking their past actions, such as opening notifications, interacting with a blog, or creating a new login in the platform. In that context, the attribution study refers to the method of grouping together all of those actions in a specific pattern to generate one desired end result.

Imagine, out of thousands of accommodations that match a user search, you have to select the “best” 25 to show to the user. Which ones would you show- the ones you know perform well or ones that have never been shown before, so that you can discover new high-potential accommodations? In the Data Science world, this is known as exploitation (continue doing what works well) versus exploration (try something new to discover hidden potential) problem and is often explained using the well-known multi-armed bandit problem. The objective of the problem is to divide a fixed number of resources between competing choices to maximize their expected gains, given that the properties of each choice are not fully known at the time of allocation.

Back in March 2022, after spending a considerable amount of effort migrating our monolithic Node.js GraphQL server from Express to Fastify, we noticed absolutely no performance improvements in production. That hit us like a bombshell, especially because Fastify performed exceptionally well in our k6 load tests in staging, where it responded to HTTP requests 107% (more than two times) faster on average than Express!

In the last few years, organisations have been increasing their investments in building Machine Learning (ML) based systems. In practice, such systems often took longer than expected to be built or failed to deliver the promised outcome. Data availability and quality have been among the most significant reasons behind this phenomenon. Since each organisation had its custom problems, open datasets or even logged data were not always directly usable. As a result, data collection and annotation processes became more crucial, yet remained under-documented.

In 2020 we started to migrate one of our most significant workloads, our Node.js based GraphQL API and many of its microservices, from our datacenter to Google Kubernetes Engine. We deploy it in three GCP regions, each having its Kubernetes cluster. Since then, our monitoring infrastructure has changed due to various periods of instability and pandemic induced scaling challenges.

As I’m writing this, we’re in the middle of our yearly load testing process.
Since a couple of years now, trivago conducts regular production load tests. We do this to test if all our services sustain the increased load we experience during the summer and winter months.
This year is now the 2nd time, where we also do another test: A "regional failover" test.