Read The Web Performance Impact Of Lossy Network Conditions
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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Read Kyle Simpson Teams up with trivago to create a JS Developer Excellence program
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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Read Presenting babel-plugin-cloudinary
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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Read RecSys Challenge 2019
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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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?

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Read A New Functional Approach to Complex Types in Apache Hive
Open Source Data Science

A New Functional Approach to Complex Types in Apache Hive

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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.

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Read Nomad - our experiences and best practices
Monitoring Backend DevOps

Nomad - our experiences and best practices

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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.

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Read Teardown, Rebuild: Migrating from Hive to PySpark
Data Science

Teardown, Rebuild: Migrating from Hive to PySpark

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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.

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