274 results for "pinot" across all locations

Uber is piloting the use of on-time rate as a new criteria for Uber Eats Pro status starting early October in select US markets.

Learn how Uber uses Apache Pinot for serving over 100 low-latency offline analytics use cases.

We engineered full SQL support on Apache Pinot to enable quick analysis and reporting on aggregated data, leading to improved experiences on our platform.

At Uber, we serve 500 million Pinot queries every day. Learn how we optimized and built an internal fork of Presto to support query features like window functions and sub-queries, all while supporting sub-second latencies at thousands of QPS.

Updating enterprise-scale software and infrastructure without creating unintentional downstream issues is a daunting task–we leveraged Apache Pinot to create Healthline, a tool for minimizing release errors in real time and at Uber scale.

With contributions from Uber and others, Apache Pinot™ now supports deletion with upsert tables! Learn how Uber drove these advancements and how you can benefit from cost-efficient infinite retention.

Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making.

Have more rows than you can count on two hands? Don’t feel like using approximations? Learn how Uber uses Apache Pinot™ to count!

Uber recently launched a new capability: Ads on UberEats. With this new ability came new challenges that needed to be solved at Uber, such as systems for ad auctions, bidding, attribution, reporting, and more.