Accelerating experimentations through Simulations

Why should you retry all tests on failure? Why not? This article will not go into details, listing pros and cons of each approach. There are already enough resources on the Web about the topic, listing valid points for both opposing views. As trivago Hotel Search frontend QA team over the last years we tried to stay away from a brute-force retry policy for failures and we rather tried to execute test retries only in selected cases. Recently, when we switched to a Continuous Deployment approach for our new frontend Web application (which empowers developers to merge and release some pull requests autonomously), we faced a greater need than before for understandable and stable test results. Due to that, showing as few “red flags” as possible for the automated checks on pull requests became even more important to ensure enough confidence in test results and to avoid slowing down the software development life cycle. The requirements and the balance between deterministic results and success ratio shifted, at least in some cases.

At trivago, our talents get to work with many of the latest technologies, including Artificial Intelligence (AI). In fact, we are actively using AI not only to enhance our day-to-day work, but also to innovate on our product. We chatted with two Product Managers at trivago, Sören Weber and Henrique Portes, who shared how their teams are experimenting with Generative AI in their projects. We discussed the challenges they faced, their key learnings, and asked them to share advice for other product managers looking to integrate AI into their product.

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)!

What triggered you to choose the career you are in and still inspires you in your path today?

Role models are often sources of aspirations and inspirations in our careers. ​​Unfortunately, in this day and age, women are still underrepresented in Tech careers. This impacts the capability of future generations, as well as those women who may want or need to reconvert into Tech professions. ​​Many teams, — and companies as a whole — do not profit from the benefits of diversity and miss out on valuable talents. A white paper by eco-Association of the Internet highlights: “In order to get more women interested in IT and technology, the visibility of female role models plays a central role. This is underlined by both studies and empirical experiences of female IT professionals” (eco - Association of the Internet) [1].

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.