Read Image Migration to Google Cloud Platform

Image Migration to Google Cloud Platform

When we think about migration, we usually imagine moving something or someone from one place to another. Doing so has inherent complexities, which can exponentially grow when migrating a software solution. In this article, we’re sharing our experience on how we migrated all our image-related infrastructure to GCP (Google Cloud Platform) from a combination of AWS (Amazon Web Services) and our Data Centre.

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  • Read Women in Tech Meetup

    Women in Tech Meetup

    Learning about risk-taking, sentiment analysis, cybersecurity, and psychological safety in one evening? That’s quite a mix, isn't it?

    That’s what the audience got offered at the Women in Tech meetup, which took place on February 29th in trivago's office space. Together with iteratec, trivago had the pleasure of bringing together the vibrant community of female tech talents and their allies on the occasion of International Women's Day, for an evening featuring stories about risk, safety, and continuous improvement.

  • Read Real-world Insights: Anomaly Detection in Internet Traffic

    Real-world Insights: Anomaly Detection in Internet Traffic

    This article is written for individuals in data science or analytics roles who are familiar with terms such as confidence interval, databases, or workflows. It is aimed at those who need to implement anomaly detection techniques for various types of users with different needs. In this article, I will share my experience from working at trivago, with a specific focus on internet traffic. Rather than delving into the details of mathematical models (as there are already many well-covered articles on this topic), I aim to provide insights into real-world situations encompassing a wide range of business needs. These situations require tailored solutions to cater to different types of stakeholders.

  • Read Accelerating experimentations through Simulations

    Accelerating experimentations through Simulations

    During the development of customer-facing applications, time is crucial, especially when it comes to testing and analyzing changes before accepting them in production. This blog post explores how we developed a Java-based reactive tool to simulate production requests, that allows us to have quicker hints about the effects of changes introduced and be more confident about the hypotheses that are formulated. As a long term vision, we wish to significantly reduce the A/B testing time and ensure seamless transitions.

  • End-to-end tests retry strategies

    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.

  • Read Experimenting with AI to Enhance Our Product: Firsthand Experience From Our Product Managers

    Experimenting with AI to Enhance Our Product: Firsthand Experience From Our Product Managers

    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.

  • Read Boosting design team efficiency: trivago’s Sketch to Figma Journey

    Boosting design team efficiency: trivago’s Sketch to Figma Journey

    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.

  • Read Building Our First GraphQL Server with Go: An Implementation Guide

    Building Our First GraphQL Server with Go: An Implementation Guide

    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.

  • Read Implementing Data Validation with Great Expectations in Hybrid Environments

    Implementing Data Validation with Great Expectations in Hybrid Environments

    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.

  • Read Tech IT Up - Growth and Learning for trivago Techies

    Tech IT Up - Growth and Learning for trivago Techies

    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)!

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