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.
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.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.
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!
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.
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.
When faced with the challenge to store, retrieve and process small or large amounts of data, structured query languages are typically not far away. These languages serve as a nice abstraction between the goal that is to be achieved and how it is actually done. The list of successful applications of this extra layer is long. MySQL users could switch from MyISAM to InnoDB or use new algorithms like Multi-Range-Read without a change to their application. We, as Hive users, can effortlessly switch our complete processing from MapReduce to, say, Tez or Spark. All this is possible because of SQL serving as an abstraction layer in between. However, in this article, I will outline the effects when SQL - specifically hiveQL - misbehaves and which steps we are taking to recover.
Tackling hard problems is like going on an adventure. Solving a technical challenge feels like finding a hidden treasure. Want to go treasure hunting with us?View all current job openings