Improving the User Experience with Uber’s Customer Obsession Ticket Routing Workflow and Orchestration Engine
Uber adopted workflow orchestration and Cadence, our open source orchestration engine, to better handle customer support ticket routing at scale.
First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.
Uber leveraged machine learning to design our capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.
In this article, we discuss Uber's journey toward a unified, multi-tenant, and scalable data workflow management system.
Aarhus Engineering Internship: Building Aggregation Support for YQL, Uber’s Graph Query Language for Grail
Uber intern Lau Skorstengaard shares his experience working on YQL, the graph query language for our in-house infrastructure state aggregation platform.
Part of Uber's open source M3 metrics system, our query engine can support real-time, large-scale computation and multiple query languages.
Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.
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.
In November 2016 Uber unveiled a sleek new rider app. The app implements a new mobile architecture across both iOS and Android. In this article, Uber Engineering discusses why we felt the need to create a new architecture pattern, and how it helps us reach our goals.
Uber Engineering explains why and how we built Chaperone, our in-house auditing system for monitoring Kafka pipeline health.
How Uber engineered Cherami, the resilient, scalable, distributed task queue system written in the Go programming language.
In this article, we take a look at Schemadock, Uber Engineering's tooling solution for managing our increasing number of MySQL clusters.
Uber Engineering explains the technical reasoning behind its switch in database technologies, from Postgres to MySQL.
Uber Engineering's Micro Deploy (μDeploy), our in-house deployment system that builds, upgrades, and rolls back services at Uber.
How Uber Engineering architected ride policies for Uber for Business, our way of verifying rides in real time.
To show how a microservice is implemented in Uber Engineering's ecosystem, we look at the development of Tincup, our currency and exchange rate service.
The details and examples of Schemaless triggers, a key feature of the datastore that’s kept Uber Engineering scaling since October 2014. This is the third installment of a three-part series on Schemaless; the first part is a design overview and the second part is a discussion of architecture.
How Uber’s infrastructure works with Schemaless, the datastore using MySQL that’s kept Uber Engineering scaling since October 2014. This is part two of a three-part series on Schemaless; part one is on designing Schemaless.
The making of Schemaless, Uber Engineering’s custom designed datastore using MySQL, which has allowed us to scale from 2014 to beyond. This is part one of a three-part series on Schemaless.
Moving away from a monolithic codebase to a service-oriented architecture (SOA) has not been an easy task. Here's a brief glimpse of the scalability problems we've faced and the steps we've taken to solve them.
What happens when you have to migrate hundreds of millions of rows of data and 100 services over several weeks with dozens of engineers, while simultaneously serving millions of rides? The story of how Uber moved to Mezzanine in 2014.