- 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