Uploaded May 2020 | Updated September 2026, 2 weeks ago
Apache Pinot is a distributed columnar database for real-time analytics. It powers many well-known applications, including LinkedIn’s Who Viewed My Profile feature and Uber Eats timely analytics. Since Pinot joined Apache Incubator in 2019, it has gained a lot of momentum with a growing community.
During this May 2020 Uber Engineering Meetup, software engineers Haibo Wang and James Shao discuss how Pinot empowers timely analytics at Uber. We will also dive into how we built an open source Presto connector to enable complex SQL queries on Pinot and other data sources, as well as how we developed upserts for Pinot to allow mutable data in real-time tables.
Learn more: eng.uber.com/engineering-sql-support-on-apache-pinot
Apache Pinot is a distributed columnar database for real-time analytics. It powers many well-known applications, including LinkedIn’s Who Viewed My Profile feature and Uber Eats timely analytics. Since Pinot joined Apache Incubator in 2019, it has gained a lot of momentum with a growing community.
During this May 2020 Uber Engineering Meetup, software engineers Haibo Wang and James Shao discuss how Pinot empowers timely analytics at Uber. We will also dive into how we built an open source Presto connector to enable complex SQL queries on Pinot and other data sources, as well as how we developed upserts for Pinot to allow mutable data in real-time tables.
Learn more: eng.uber.com/engineering-sql-support-on-apache-pinot

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During this April 2019 meetup, Uber engineer Mingshi Wang discusses the architectures of Michelangelo (MA) learners and transformers. A Michelangelo (MA) Learner is a workflow authoring framework for Ubers Michelangelo machine learning platform that allows Uber data scientists, researchers, and engineers to solve complex machine learning problems with customized workflows. An MA Learner provides Python SDKs that make it easy to write MA models on Jupiter notebooks while hiding the underlying complexities of distributing the machine learning jobs to different computing environments. At Uber, machine learning models are represented by pipelines composed of MA transformers. The data preparation and training processes that involve one or more estimators produce a trained pipeline with MA transformers. The trained pipelines are persisted for subsequent usage by batch and online predictions. [Uber Seattle] Michelangelo (MA) Learners and Transformers](https://i.ytimg.com/vi/I07n5qHnrpo/mqdefault.jpg)
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Learn more about our experimentation platform: https://eng.uber.com/xp/ [Uber Marketplace] Marketplace Experimentation Vivek Trehan](https://i.ytimg.com/vi/IR000RqN7pw/mqdefault.jpg)
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The domain of Payments, Finance, and more generally FinTech, is a fast-growing industry that reached record global investment of $111.8B in 2018. The underlying technology that powers this incredible growth is also evolving rapidly. Hosted by Uber, MoneyCon 2019 brought together engineers from leading tech companies to present on the latest topics in payments engineering.
In this talk during MoneyCon 2019, Squares Anthony Bishopric discusses how Square applied a novel cryptographic concept and classic double entry accounting principles to process and disburse hundreds of millions of dollars per day. [MoneyCon 2019] Books: Scalable, Flexible, and Immutable Storage of Squares Financials](https://i.ytimg.com/vi/I_Pt_i3ntGw/mqdefault.jpg)