Real-Time Exactly-Once Ad Event Processing with Apache Flink, Kafka, and Pinot
Uber recently launched a new capability: Ads on UberEats. With this new ability came new challenges that needed to be solved at Uber, such...
Jellyfish: Cost-Effective Data Tiering for Uber’s Largest Storage System
Problem
Uber deploys a few storage technologies to store business data based on their application model. One such technology is called Schemaless, which enables the...
Streaming Real-Time Analytics with Redis, AWS Fargate, and Dash Framework
Introduction
Uber’s GSS (Global Scaled Solutions) team runs scaled programs for diverse products and businesses, including but not limited to Eats, Rides, and Freight. The...
How Uber Achieves Operational Excellence in the Data Quality Experience
Uber delivers efficient and reliable transportation across the global marketplace, which is powered by hundreds of services, machine learning models, and tens of thousands...
Uber’s Fulfillment Platform: Ground-up Re-architecture to Accelerate Uber’s Go/Get Strategy
Introduction to Fulfillment at Uber
Uber’s mission is to help our consumers effortlessly go anywhere and get anything in thousands of cities worldwide. At its...
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...
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...
Elastic Distributed Training with XGBoost on Ray
Introduction
Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases...
Efficient and Reliable Compute Cluster Management at Scale
Introduction
Uber relies on a containerized microservice architecture. Our need for computational resources has grown significantly over the years, as a consequence of business’ growth....
Handling Flaky Unit Tests in Java
Introduction to Flaky Tests
Unit testing forms the bedrock of any Continuous Integration (CI) system. It warns software engineers of bugs in newly-implemented code and...
Scaling of Uber’s API gateway
As a recap from the last article, Uber’s API Gateway provides an interface and acts as a single point of access for all of...
The Architecture of Uber’s API gateway
API gateways are an integral part of microservices architecture in recent years. An API gateway provides a single point of entry for all our...
Flipr: Making Changes Quickly and Safely at Scale
Introduction
Uber’s many software systems require a high volume of changes every day. Because of our systems’ size and complexity, it is a significant challenge...
Designing Edge Gateway, Uber’s API Lifecycle Management Platform
The making of Edge Gateway, the highly-available and scalable self-serve gateway to configure, manage, and monitor APIs of every business domain at Uber.
Evolution of...
Introducing Domain-Oriented Microservice Architecture
Introduction
Recently there has been substantial discussion around the downsides of service oriented architectures and microservice architectures in particular. While only a few years ago,...
Why We Leverage Multi-tenancy in Uber’s Microservice Architecture
Multi-tenancy lets Uber tag requests coming into our microservice architecture, giving us the flexibility to route requests to specific components, such as during testing scenarios.
Engineering SQL Support on Apache Pinot at Uber
We engineered full SQL support on Apache Pinot to enable quick analysis and reporting on aggregated data, leading to improved experiences on our platform.
Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction
In 2019, Uber's Infrastructure team built new services and systems to enable resource savings, efficiency gains, and greater resilience across our technology stack.
Uber’s Data Platform in 2019: Transforming Information to Intelligence
In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.
Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
