![[Payments Platform] Evolution of Payments at Uber Nimish Sheth & Steven Karis @UberEngineering](https://i.ytimg.com/vi/Dz6dAZs8Scg/maxresdefault.jpg)
Uploaded November 2018 | Updated September 2026, 2 weeks ago
Uber’s payments architecture is composed of two main parts: collections and disbursements. In this presentation from a September 2018 meetup, engineers Nimish Sheth & Steven Karis offers a closer look at our high-level payments stack, core data models, and cash money movements.
Uber’s payments architecture is composed of two main parts: collections and disbursements. In this presentation from a September 2018 meetup, engineers Nimish Sheth & Steven Karis offers a closer look at our high-level payments stack, core data models, and cash money movements.
![[Payments Platform] Evolution of Payments at Uber Nimish Sheth & Steven Karis](https://i.ytimg.com/vi/Dz6dAZs8Scg/mqdefault.jpg "[Payments Platform] Evolution of Payments at Uber Nimish Sheth & Steven Karis
Uber’s payments architecture is composed of two main parts: collections and disbursements. In this presentation from a September 2018 meetup, engineers Nimish Sheth & Steven Karis offers a closer look at our high-level payments stack, core data models, and cash money movements.")
![[Uber Open Source] Leveraging Pinot at Uber for Large-scale Analytics](https://i.ytimg.com/vi/E8Xsmc9AqDM/mqdefault.jpg "[Uber Open Source] Leveraging Pinot at Uber for Large-scale Analytics
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: https://eng.uber.com/engineering-sql-support-on-apache-pinot/")
![[Uber Eng Blog] Visualizing Uber Air Simulated Flights](https://i.ytimg.com/vi/Edr0Adlka-Y/mqdefault.jpg "[Uber Eng Blog] Visualizing Uber Air Simulated Flights
In this visualization from the Tuesday, October 29, 2019 Uber Engineering Blog article, Taking City Visualization into the Third Dimension with Point Clouds, 3D Tiles, and deck.gl, Uber Air flight routes are simulated using our visualization suite.
Learn more about these powerful data visualizations tools on the Uber Engineering Blog: eng.uber.com")
![[Go NYC] The Myth around Go Frameworks & Libraries Venkatesh Thallam](https://i.ytimg.com/vi/EiBHiMexzEI/mqdefault.jpg "[Go NYC] The Myth around Go Frameworks & Libraries Venkatesh Thallam
During an October 2018 Go Language NYC meetup, Paperless Post engineer Venkatesh Thallam discusses apprehensions using Go frameworks and external libraries and offers suggestions to increase Go development flexibility.")
![[Destination:Web] Fusion.js: A Plugin-based Universal Web Framework](https://i.ytimg.com/vi/Enzc0D8EI_8/mqdefault.jpg "[Destination:Web] Fusion.js: A Plugin-based Universal Web Framework
During this September 2018 Destination:Web meetup, Uber engineer Leo Horie discusses Fusion.js, Ubers open source, plugin-based universal web framework to build lightweight, high-performing web apps.")
![[Uber Open Summit Sofia 2019] Metrics at Uber](https://i.ytimg.com/vi/GYmDVsNueY4/mqdefault.jpg "[Uber Open Summit Sofia 2019] Metrics at Uber
During this Uber Open Summit Sofia 2019 presentation, engineer Celina Ward discusses why we built M3, our open source metrics stack, and M3DB, our open source distributed time series database. She also walks through how to use M3 and the future of its development roadmap.
Learn more about Uber Open Summit Sofia 2019: https://uberopensofia.splashthat.com/")
![[Visualization Nights] Tutorial#1: Comparing Speeds Across Time Periods](https://i.ytimg.com/vi/Gh1XWyTWpCE/mqdefault.jpg "[Visualization Nights] Tutorial#1: Comparing Speeds Across Time Periods
During this May 2019 meetup in New York City, the Uber Visualization team offers a basic tutorial on how to download the Uber Movement Street Speeds dataset and visualize it in Kepler.gl. Through this tutorial, they explore spatiotemporal insights through the lens traffic speed data during two different time periods.")
![[Uber Seattle] Michelangelo (MA) Learners and Transformers](https://i.ytimg.com/vi/I07n5qHnrpo/mqdefault.jpg "[Uber Seattle] Michelangelo (MA) Learners and Transformers
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 Marketplace] Marketplace Experimentation Vivek Trehan](https://i.ytimg.com/vi/IR000RqN7pw/mqdefault.jpg "[Uber Marketplace] Marketplace Experimentation Vivek Trehan
During a May 2018 meetup, engineer manager Vivek Trehan discusses how we can improve features and services on our platform using our experimentation platform.
Learn more about our experimentation platform: https://eng.uber.com/xp/")
