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Cadence Multi-Tenant Task Processing

Introduction Cadence is a multi-tenant orchestration framework that helps developers at Uber to write fault-tolerant, long-running applications, also known as workflows. It scales horizontally to...

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,...

Improving HDFS I/O Utilization for Efficiency

Scaling our data infrastructure with lower hardware costs while maintaining high performance and service reliability has been no easy feat. To accommodate the exponential...

Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner

  Introduction The Fulfillment Platform is a foundational Uber domain that enables the rapid scaling of new verticals. The platform handles billions of database transactions each...

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...

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...

Enabling Seamless Kafka Async Queuing with Consumer Proxy

Uber has one of the largest deployments of Apache Kafka in the world, processing trillions of messages and multiple petabytes of data per day....

Building Scalable Streaming Pipelines for Near Real-Time Features

Background Uber is committed to providing reliable services to customers across our global markets. To achieve this, we heavily rely on machine learning (ML) to...

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