How Uber Migrated Financial Data from DynamoDB to Docstore
Introduction
Each day, Uber moves millions of people around the world and delivers tens of millions of food and grocery orders. This generates a large...
Introducing uGroup: Uber’s Consumer Management Framework
Background
Apache Kafka® is widely used across Uber’s multiple business lines. Take the example of an Uber ride: When a user opens up the Uber app,...
Pinot Real-Time Ingestion with Cloud Segment Storage
Introduction
Apache Pinot is an open source data analytics engine (OLAP), which allows users to query data ingested from as recently as a few seconds...
Containerizing Apache Hadoop Infrastructure at Uber
Introduction
As Uber’s business grew, we scaled our Apache Hadoop (referred to as ‘Hadoop’ in this article) deployment to 21000+ hosts in 5 years, to...
‘Orders Near You’ and User-Facing Analytics on Real-Time Geospatial Data
Introduction
By its nature, Uber’s business is highly real-time and contingent upon geospatial data. PBs of data are continuously being collected from our drivers, riders,...
Analyzing Customer Issues to Improve User Experience
Introduction
The primary goal for customer support is to ensure users’ issues are addressed and resolved in a timely and effective manner. The kind of...
Customer Support Automation Platform at Uber
High Level Overview of the Problem
Introduction
If you’ve used any online/digital service, chances are that you are familiar with what a typical customer service experience...
Freight Pricing with a Controlled Markov Decision Process
Intro
Uber Freight was launched in 2017 to revolutionize the business of matching shippers and carriers in the huge and inefficient freight trucking industry (around...
Revolutionizing Money Movements at Scale with Strong Data Consistency
Uber as a platform invites its users to leverage it, earn from it, and be delighted by it. Serving more than 18 million requests...
