Uber Engineering

Overview

Uber runs ride-hailing, delivery, and freight as global real-time marketplaces where every dispatch is a stateful workflow that must finish in seconds, and the engineering blog reflects that operational discipline. Their strongest content covers database operations at marketplace scale (running MySQL and Cassandra across regions, plus the LedgerStore that backs trillions of indexed financial entries), real-time stream processing built on Kafka, Flink, and Pinot for ad events with exactly-once semantics, and ML systems for ETA prediction and push optimization. For interviews, 'How Uber Serves Over 40 Million Reads Per Second' is excellent prep for any high-read cache-fronted design, 'How Uber Predicts Arrival Times Using Deep Learning' (DeepETA) anchors ride-hailing ETA questions, and 'Real-Time Ad Event Processing with Flink, Kafka, and Pinot' is a streaming-pipeline reference.

Interview questions

• What topics does Uber engineering blog cover?
• Why is Uber's blog valuable for system design interviews?
• Which Uber posts are most relevant to ride-hailing system design questions?

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