DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data Lake
Uber engineers discuss the development of DBEvents, a change data capture system designed for high data quality and freshness that is capable of operating on a global scale.
Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.
Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.
Uber Engineering's XP Background Push mitigates bugs safely and efficiently in real time, facilitating more seamless user experiences on our apps.
How Uber Engineering re-architected the content delivery feed and backend ecosystem of our new driver app to deliver an enhanced user experience.
How Uber engineered Cherami, the resilient, scalable, distributed task queue system written in the Go programming language.
The end of a two-part series on the tech stack that Uber Engineering uses to make transportation as reliable as running water, everywhere, for everyone, as of spring 2016.