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.