Detecting Abuse at Scale: Locality Sensitive Hashing at Uber Engineering
In this article, we discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.
In this article, we discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.
Uber Engineering's data center architecture is adopting IPv6 support to keep pace with the expansion of our services.
The Uber Developer Platform empowers engineers worldwide to build moving in-app experiences for riders and drivers through integrations with Uber's Trip Experience API.
Say cheese! To better identify driver fraud, Uber Engineering's safety team developed Real-Time ID Check, a face verification solution that uses Face API.
Uber Engineering's fraud prevention team built the Mastermind rules engine to detect highly evolved forms of fraud at large scale in a fraction of a second.
An Uber incident response engineer discusses why we built our own custom email IDS to help guard against well executed phishing campaigns.
In this article, we discuss deck.gl, an open sourced, WebGL-powered framework specifically designed for exploring and visualizing data sets at scale.
In this article, we take a look at Euclid, Uber Engineering's Hadoop and Spark-based in-house marketing platform.
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.
Take a look into uReplicator, Uber’s open source solution for replicating Apache Kafka data in a robust and reliable manner.
The end of a two-part series on the tech stack that Uber Engineering uses to make transportation as reliable as running water, everywhere, for everyone, as of spring 2016.
Uber’s mission is transportation as reliable as running water, everywhere, for everyone. Here's the first of a two-part series on the tech stack that Uber Engineering uses to make this happen.
An Uber engineer discusses how introducing cash payments for rides has fostered Uber's growth in India and beyond.
How the Go programming language helped Uber Engineering build and scale our highest queries per second microservice, for geofence lookups.
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.
How to develop with Uber Engineering's Ringpop, an open source library developed to make our applications cooperative and scalable.
How the Uber for Business engineering team built our ride profiles feature to seamlessly toggle between business and personal trips, which debuted Fall 2015.
If you think you could out-solve Uber Engineering, here’s the chance to prove it. We’ve partnered with CodeFights to bring you the #UberBot.