Read Automation-First Approach Using the Karate API Testing Framework
Open Source Backend

Automation-First Approach Using the Karate API Testing Framework

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Adopting an automation-first mindset is the first step to reduce manual and repetitive work. Thinking this way enables us to move faster, and more efficiently. It unburdens us from mundane, repetitive work, allowing us to focus on solving problems and creating value in the Software Development Life Cycle. So the first thing is to look for a tool that helps us write automated tests faster and is easy to maintain.

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Engineering Culture Frontend

triversity - An Interview with two trivago Tech Camp Participants

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Gyumin Lee and Eunae Jang were participants in this year’s trivago Tech Camp. We did an interview with them to learn about their experiences and get some insights into the project development of triversity - a project management tool for university collaboration. Can you quickly introduce yourselves? Hi! My name is Eunae Jang and I’m completing a Bachelor’s in Computer Science at RWTH Aachen University. I will start my Master’s in CS from next semester.

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User Experience Data Science

How to Analyze SurveyMonkey Data in Python

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Introduction As a user researcher, it is important to know more about our users and their preferences concerning our product. One way to do that is by conducting surveys. In order to gather user feedback from our global markets, we need to conduct a survey with a slightly different set of questions/translations for different countries, and then analyze the results and compare if there is any difference across countries concerning user needs.

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Data Science Engineering

Machine Learning and Bathtubs - How Small Visual Changes Improve User Experience

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While searching for “Spa and Wellness hotels in Berlin…” I land on trivago. Surprisingly the main images of the hotels exactly reflect the spa concept that I am searching for. It helped me better compare hotels on the list for finding my ideal accommodation for my vacation! This was the user experience we were looking for when we kicked off the Image Concepts project at trivago. The users with clear hotel search intent who are looking for a specific concept hotel before coming to trivago are redirected to the landing pages related to that particular topic.

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Monitoring Frontend User Experience

The Web Performance Impact Of Lossy Network Conditions

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tl;dr: continuously monitor your CDN and origin servers on layer 3 with tools like MTR. Layer 3 issues on external middleware can have a significant impact on layer 7 web performance. In a recent rollout of a new cloud service, we monitored the impact of this service on web performance, UX and business metrics. For all cloud regions and origin servers, we had Synthetic and Real User Monitoring for our site in place.

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Engineering Culture Frontend

Kyle Simpson Teams up with trivago to create a JS Developer Excellence program

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We have all seen the job ads that look for a “developer with at least 8 years of experience and a Computer Science degree”, a “JavaScript rockstar”, or somebody with “excellent command of technologies A, B, C, D, and E”. They are annoying in two ways. First, they are unrealistic. In today’s software developer job market, somebody fitting the above descriptions usually does not have to look for a job. Rather, the job has to find them.

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Frontend Open Source

Presenting babel-plugin-cloudinary

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I’m happy to let you know that we are releasing trivago/babel-plugin-cloudinary to the open source community! Throughout this article I will explain to you the motivation behind this project and how it works in detail. At trivago, we use images to a large degree to enable our users to get a visual impression of the accommodations that they’re interested in. We all want to see beautiful and good quality pictures so we can have a better feeling about the place where we are going to.

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Data Science

RecSys Challenge 2019

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Our data scientists and engineers love the challenges that their work presents to them on a daily basis and thrive in our agile environment where they can share their knowledge, learn from others, and work together to solve any problems that arise. We are always looking for ways to share the unique problem settings we encounter and to inspire a productive exchange on algorithm development and evaluation. That is why we have partnered with researchers from TU Wien, Politecnico di Milano, and Karlsruhe Institute of Technology to launch the RecSys Challenge 2019, the annual data science challenge of the ACM Recommender Systems conference.

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