Responsible for cleaning, storing, and serving over 100 petabytes of analytical data, Uber's Hadoop platform ensures data reliability, scalability, and ease-of-use with minimal latency.
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
Shan He, the technical lead on Uber's kepler.gl framework, discusses her journey to data visualization and why she believes open source is such an important part of her team's work.
Uber's Data Infrastructure team overhauled our approach to scaling our storage infrastructure by incorporating several new features and functionalities, including ViewFs, NameNode garbage collection tuning, and an HDFS load management service.
Written in Haskell, Queryparser is Uber Engineering's open source tool for parsing and analyzing SQL queries that makes it easy to identify foreign-key relationships in large data warehouses.
Migrating our Schemaless sharding layer from Python to Go while in production demonstrated that it was possible for us to rewrite the frontend of a massive datastore with zero downtime.
The Uber Insurance Engineering team extended Kafka’s role in our existing event-driven architecture by using non-blocking request reprocessing and dead letter queues (DLQ) to achieve decoupled, observable error-handling without disrupting real-time traffic.
Uber's mobile engineers leverage code generation to make our applications more reliable and boost developer productivity.
Get to know Uber Aarhus Engineering and the work they do behind the scenes to build and maintain our storage and compute platforms.
As we approach the New Year, Uber Open Source revisits some of Uber Engineering's most popular projects from 2017.
Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.
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 shares our best practices for working with plugins, a powerful tool that enables us to build and ship features quickly at scale.
Uber Engineering built a new microservice to power Driver Profiles, an in-app platform that enhances the Uber experience by celebrating drivers.
Learn how Uber Engineering’s Employee Productivity Tools team built uChat, an internal chat solution capable of scaling to meet the needs of our growing global company.
Uber Engineering architected a real-time trip features prediction system using an open source RESTful search engine built with Elasticsearch, Logstash, and Kibana (ELK).
Uber Engineering built Uber Central's architecture by integrating the Uber for Business platform with a custom front-end design tailored to customer feedback.
In this article, a software engineer on Uber Engineering's Payments Efficiency Team discusses how we optimized our driver payment platform for cash and digital wallet commissions in India.
In this article, Uber Engineering shares our best practices for relieving RxJava backpressure on Android through targeted operators, more forgiving RxJava 1.x configurations, and RxJava 2.x.
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