At trivago we live diversity. We have 55 localised platforms and internally you can find talents from around 90 different nationalities all working towards providing a better experience to our customers. We are constantly evolving as we face organisational, societal and industrial challenges. That's why we identify a lot with this year's theme "A New Dawn", as we too explore the meaning and evolution of our approaches and practices. This year we have decided to support IxDA20 through sponsorship for the first time. It reflects our belief and increasing efforts to invest in Design and Research at trivago as we strive for an inclusive world.
We strongly believe in sharing knowledge not only internally, but also with the tech community around the world. This is one of the reasons why we support Open Source software through development and sponsorship. For example, we are the second biggest supporter on Open Collective and we have a ton of our own Open Source projects too. (Check out our Open Source page for more info.)
Make was created in 1976 by Stuart Feldman at Bell Labs to help build C programs. But how can this 40+ year old piece of software help us develop and maintain our ever-growing amount of cloud-based microservices?
trivago Intelligence was born in 2013 with two main objectives: First, to provide bidding capability to the advertisers, who are listed on trivago, and second, to provide them with metrics related to their own hotels; like clicks, revenue, and bookings (typical BI data). This project faced a wave of inevitable data growth which lead to a refactoring process which produced a lot of learnings for the team. As I expect it to be useful for other teams who deal with similar challenges, this article will describe why a team started a full migration of technologies, how we did it and the result of it.
When we announced our renewal of our investment in Webpack and Babel last year, I found a tweet from Guillermo Rauch, CEO of zeit.co, one of the most interesting serverless computing companies at the moment.
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.
We are originally from South Korea and we've been in Germany for about three years.
We often check the Facebook and Instagram posts from Life at trivago, so we could easily find out about trivago Tech Camp 2019 through social media.
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.
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!
At trivago, we have several workflows which interact with external services. The health and availability of external services can have an impact on keeping our workflows alive and responsive. Think of an API call made to an external service which is down. Our workflows have to be prepared to expect these errors and adapt to it.
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.
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.