Quality and testing go hand in hand, and in 2023 we took on a new and exciting challenge to change how we test our mobile applications. Specifically, we are training ML models to test our applications…

The financial accounting services platform at Uber operates at an internet scale– approximately 1.5 billion journal entries (JEs) per day and 120 million transactions per day via ETL and data…

Uber Eats’ mission is to make eating effortless, at any time, for anyone. The Uber Eats home feed is an important tool for fulfilling this goal, as it aims to provide a magical food browsing…

Rendering large lists in React can be a challenging task for developers. As the size of the list grows, the DOM (Document Object Model) tree also grows, leading to performance issues like slow…

Uber uses open-source Presto to query nearly every data source, both in motion and at rest. Presto’s versatility empowers us to make intelligent, data-driven business decisions. We operate around 20…

The Uber Michelangelo feature store, called Palette, is a database of Uber-specific curated and internally crowd-sourced features that are easy to use in machine learning projects. It comes to solve…

As a marketplace-based, consumer-facing app, Uber encounters a multitude of sources of fraud across its platform. In one of the most common cases of fraud, bad actors use various methods to attempt to…

Discover real-time query analytics and governance with DataCentral: Uber’s big data observability powerhouse, tackling millions of queries in petabyte-scale environments.

Uber’s mission is to reimagine the way the world moves for the better and provide earning opportunities globally through its marketplace. One effective approach to bring the Uber brand and marketplace…

Docstore is Uber's in-house, distributed database built on top of MySQL®. Storing tens of PBs of data and serving tens of millions of requests/second, it is one of the largest database engines at Uber…

Uber is a global business, and has a customer base that’s spread throughout the world. Uber’s customer base is divided into many user personas, predominantly riders, drivers, eaters, couriers, and…

If you were to ask a veteran SOC (Security Operations Center) analyst about Network IDS (Intrusion Detection Systems) or IPS (Intrusion Prevention Systems), the response would probably contain phrases…

This blog post describes Uber's journey towards utilizing hardware efficiently via better load balancing. The work described here lasted over a year, involved engineers across multiple teams, and…

Uber has one of the largest Apache HadoopⓇ Distributed File System (HDFS) deployments in the world, with exabytes of data across tens of clusters. HDFS team at Uber had to solve the problem of…

With the introduction of Model Excellence Scores at Uber, we're setting a new standard for measuring, monitoring, and maintaining ML model quality–read how this innovative approach aims to enhance ML…

Machine Learning (ML) is celebrating its 8th year at Uber since we first started using complex rule-based machine learning models for driver-rider matching and pricing teams in 2016. Since then, our…