Uber Engineering Blog (Links)

• Designing Uber
• Vertical CPU Scaling: Reduce Cost of Capacity and Increase Reliability
• Design the Uber backend: System design walkthrough (educative)
• The Uber Engineering Tech Stack, Part I: The Foundation
• Uber’s Fulfillment Platform: Ground-up Re-architecture to Accelerate Uber’s Go/Get Strategy
• Bringing what’s new from teams across Uber to life
• Introducing Domain-Oriented Microservice Architecture
• Customer Support Automation Platform at Uber
• CRISP: Critical Path Analysis for Microservice Architectures
• Operating Apache Pinot @ Uber Scale
• ‘Orders Near You’ and User-Facing Analytics on Real-Time Geospatial Data
• Elastic Distributed Training with XGBoost on Ray
• Why Uber Engineering Switched from Postgres to MySQL
• Enabling Seamless Kafka Async Queuing with Consumer Proxy
• Building Reliable Reprocessing and Dead Letter Queues with Apache Kafka
• How Uber is Leveraging Apache Kafka For More Than 300 Micro Services
• Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development
• Supercharging A/B Testing at Uber
• Jellyfish: Cost-Effective Data Tiering for Uber’s Largest Storage System
• YAML Generator for Funnel YAML Files: Streamlining the Mobile Data Workflow Process
• Real-Time Exactly-Once Ad Event Processing with Apache Flink, Kafka, and Pinot
• Introducing AresDB: Uber’s GPU-Powered Open Source, Real-time Analytics Engine
• Time Series Forecasting @Uber
• Forecasting @UBER
• Deep and Confident Prediction for Time Series at Uber
• Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner
• UBER State Machine – Trips
• Design the Uber backend: System design walkthrough
• SED : Uber State Machine with Uday Kiran Medisetty
• Architecting with Google Cloud
• DeepETA: How Uber Predicts Arrival Times Using Deep Learning
• Faster Neural Networks Straight from JPEG
• Uber’s Next Gen Push Platform on gRPC
• Uber Design GKCS