The CTO Podcast with Fexingo · 2026-08-06 · 13 min
Uber's real-time data platform processes trillions of events daily, but by 2024 the cost of that infrastructure was ballooning. This episode dissects the migration that slashed their data storage and compute spend by 50 percent - moving from a monolithic streaming architecture to a tiered, disaggregated model built on Apache Pinot and an in-house engine called Speedy. Lucas and Luna break down the trade-offs: why they chose Pinot over Druid, how they rethought data lifecycle with hot, warm, and cold tiers, and the hard lessons about operational complexity when you run two systems in parallel for nearly a year. They also unpack the cultural shift - how the team convinced skeptical engineers to let go of a system they'd built over a decade. If you're running any data-heavy platform, this is a masterclass in cost optimization without sacrificing real-time guarantees.
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