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Yupeng Fu

3 BLOG ARTICLES 0 RESEARCH PAPERS
Yupeng Fu is a Staff Software Engineer at Uber on the Data Analytics team. He leads several streaming teams building scalable, reliable, and performant streaming solutions. Yupeng is an Apache Pinot committer.

Engineering Blog Articles

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, demand and supply data are aggregated in Kafka queues to serve fare calculations.

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 as systems for ad auctions, bidding, attribution, reporting, and more. This article focuses on how we

‘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, restaurants, and eaters. Real-time analytics over this geospatial data could provide powerful insights.

In this

Disaster Recovery for Multi-Region Kafka at Uber

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Apache Kafka at Uber

Uber has one of the largest deployments of Apache Kafka in the world, processing trillions of messages and multiple petabytes of data per day. As Figure 1 shows, today we position Apache Kafka as a cornerstone

Operating Apache Pinot @ Uber Scale

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Introduction

Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to

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