Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.
Composed of a staged rollout and intelligent analytics tool, Uber Engineering's experimentation platform is capable of stably deploying new features at scale across our apps. In this article, we discuss the challenges and opportunities we faced when building this product.
A daylong event at Uber’s Palo Alto office, sponsored by our LadyEng group, showcased the technical work across Uber Engineering as well as the people who are leading and building these projects. Here are some of the resulting presentations.
Uber Engineering debuts deck.gl 4.0, the latest version of our open source data visualization framework featuring enhanced geospatial exploration, a re-architected codebase, and more comprehensive documentation.
Although an untraditional choice for building mobile architectures, deep scope hierarchies are a key component of Uber's new Android rider app that enable the quick and seamless rollout of new features.
Uber Engineering's data processing platform team recently built and open sourced Hoodie, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hoodie powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.
In November 2016 Uber unveiled a sleek new rider app. The app implements a new mobile architecture across both iOS and Android. In this article, Uber Engineering discusses why we felt the need to create a new architecture pattern, and how it helps us reach our goals.
Seemingly small inefficiencies are greatly magnified as Uber's business scales. In this article we’ll explore design considerations and unique implementation characteristics of Pyflame, Uber Engineering's high-performance Python profiler implemented in C++.
A behind-the-scenes look at how Uber Engineering continues to develop our virtual onboarding funnel which enables hundreds of thousands of driver-partners to get on the road and start earning money with Uber.
Fraud prevention is one of Uber's fastest growing areas of research and development. As our platform has grown, so has the international underworld that tries to undermine it. Here’s how Uber engineers systems to fight fraud in 2016 and beyond.
Imagine you have to store data whose massive influx increases by the hour. Your first priority, after making sure you can easily add storage capacity, is to try and reduce the data’s footprint to save space. But how? This is the story of Uber Engineering’s comprehensive encoding protocol and compression algorithm test and how this discipline saved space in our Schemaless datastores.
The details and examples of Schemaless triggers, a key feature of the datastore that’s kept Uber Engineering scaling since October 2014. This is the third installment of a three-part series on Schemaless; the first part is a design overview and the second part is a discussion of architecture.
How Uber’s infrastructure works with Schemaless, the datastore using MySQL that’s kept Uber Engineering scaling since October 2014. This is part two of a three-part series on Schemaless; part one is on designing Schemaless.
Looking for that extra edge in the tech interview process? Uber Technical Recruiting gives their perspective on what they're looking for, and how software engineers can maximize their odds of getting noticed.
Moving away from a monolithic codebase to a service-oriented architecture (SOA) has not been an easy task. Here's a brief glimpse of the scalability problems we've faced and the steps we've taken to solve them.
When do people most frequently request rides? Using Uber Data in 2014, we see how cities around the world have different rhythms of movement. Here's the pulse of New York City, London, Los Angeles, San Francisco, Chicago and Miami.
When you work at one of the fastest-growing companies in the world, you get used to building some pretty awesome things. Our internal #LadyEng organization was founded in 2014 to improve the recruitment process, career opportunities, and general work environment for women engineers and other technical roles. Here, we highlight a few of our members and what they've been working on over the past year.
What happens when you have to migrate hundreds of millions of rows of data and 100 services over several weeks with dozens of engineers, while simultaneously serving millions of rides? The story of how Uber moved to Mezzanine in 2014.