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.
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.
Hello from trivago’s performance & monitoring team. One important part of our job is to ship more than a terabyte of logs and system metrics per day, from various data sources into elasticsearch, several time series databases and other data sinks. We do so by reading most of the data from multiple Kafka clusters and processing them with nearly 100 Logstashes. Our clusters currently consists of ~30 machines running Debian 7 with bare-metal installations of the aforementioned services.
Machine Learning (ML) engineering and software development are both fundamentally about writing correct and robust algorithms. In ML engineering we have the extra difficulty of ensuring mathematical correctness and avoiding propagation of round-off errors in the calculations when working with floating-point representations of a number. As such, ML engineering and software development share many challenges… and some of the solutions to these. Concepts like unit testing and continuous integration rapidly found its way into the jargon and the toolset commonly used by data scientist and numerical scientist working on ML engineering.
What’s the point in downloading the app from the store when you can do the same thing in the browser? I’m a product designer at trivago, and would like to share some insights into one of our biggest projects we tackled in the last period. Designing trivago’s new mobile app!
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
It is not everyday that you get to chat face to face with the creator of a highly relevant open source project. Accordingly, we were highly anticipating a certain visit in mid-October, 2018.
Sometimes, when I look back over the time I have spent working at trivago, I see how it changed my life and how lucky I have been to get the chance to work among this amazing community, to live and to learn with them. I look back and see a younger version of myself looking desperately for something different and, by just sheer luck, getting it.
Testing your functionality is important, but what happens if other factors come into play? In this blog post we show how trivago handles non-functional testing for every commit and how we scaled it.