Less is More: Engineering Data Warehouse Efficiency with Minimalist Design
Data science helps Uber determine which tables in a database should be off-boarded to another source to maximize the efficiency of our data warehouse.
Migrating Functionality Between Large-scale Production Systems Seamlessly
With zero downtime, Uber's Payments Engineering team embarked on a migration that would allow authorization hold logic to be written once and used across existing and future payments products.
Employing QUIC Protocol to Optimize Uber’s App Performance
Implementing QUIC protocol against TCP over cellular networks on our apps led to a reduction of 10-30 percent in tail-end latencies for HTTP traffic.
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.
Open Sourcing Peloton, Uber’s Unified Resource Scheduler
First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.
Using Machine Learning to Ensure the Capacity Safety of Individual Microservices
Uber leveraged machine learning to design our capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.
Managing Uber’s Data Workflows at Scale
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.
The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series...
Part of Uber's open source M3 metrics system, our query engine can support real-time, large-scale computation and multiple query languages.
Observability at Scale: Building Uber’s Alerting Ecosystem
Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.
Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads
Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.
Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
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.
Maximizing Process Performance with Maze, Uber’s Funnel Visualization Platform
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, Large-scale Metrics Platform for Prometheus
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
Databook: Turning Big Data into Knowledge with Metadata at Uber
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
JVM Profiler: An Open Source Tool for Tracing Distributed JVM Applications at Scale
Uber open sourced JVM Profiler, our distributed profiler, to enable others to seamlessly collect JVM performance and resource usage metrics.
Engineering a Job-based Forecasting Workflow for Observability Anomaly Detection
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.
Scaling for Growth: A Q&A with Uber’s VP of Core Infrastructure, Matthew Mengerink
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.
Scaling Uber’s Apache Hadoop Distributed File System for 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.
Introducing QALM, Uber’s QoS Load Management Framework
Uber Engineering built QALM, a smart load management tool allowing for graceful degradation by preserving critical system requests and shedding non-critical requests.