Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.
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.
Uber developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.
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.
Uber open sourced JVM Profiler, our distributed profiler, to enable others to seamlessly collect JVM performance and resource usage metrics.
Uber’s Observability Applications team overhauled our anomaly detection platform’s workflow to enable the intuitive and performant backfilling of forecasts, paving the way for more intelligent alerting.
Matthew Mengerink, Vice President of Engineering for Uber’s Core Infrastructure group, talks about how converging technologies and cloud computing contribute to stable and scalable growth.
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.
Uber Engineering built QALM, a smart load management tool allowing for graceful degradation by preserving critical system requests and shedding non-critical requests.
Uber Engineering built Grail, our infrastructure management platform, to aggregate the current state of our systems into a single global view, spanning all zones and regions.
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 Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.
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.
Uber Engineering's data processing platform team recently built and open sourced Hudi, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hudi powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.
This article is about developing Uber Engineering's open source distributed tracing system, Jaeger.