Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction The Fulfillment Platform is a foundational Uber domain that enables the rapid scaling of new verticals. The platform handles billions of database transactions each day, ranging from user actions (e.g., a driver starting a trip) and system actions (e.g., cre
Pyflame: Uber Engineering's Ptracing Profiler for Python
EngineeringPyflame: Uber Engineering’s Ptracing Profiler for PythonSeptember 27, 2016 / Global At Uber, we make an effort to write efficient backend services to keep our compute costs low. This becomes increasingly important as our business grows; seemingly small inefficiencies are greatly magnified at Uber’s scale. We’ve found flame graphs to be an effective tool for understanding the CPU and mem
Food Discovery with Uber Eats: Building a Query Understanding Engine
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Choice is fundamental to the Uber Eats experience. At any given location, there could be thousands of restaurants and even more individual menu items for an eater to choose from. Many factors can influence their choice. For example, the time of day, their cuisine prefer
Introducing Athenadriver: An Open Source Amazon Athena Database Driver For Go
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Data analytics play a critical part in Uber’s decision making, driving and shaping all aspects of the company, from improving our products to generating insights that inform our business. To ensure timely and accurate analytics, the aggregated, anonymous data that power
Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing
Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing At Uber, we use robust data processing systems such as Apache Flink and Apache Spark to power the streaming applications that helps us calculate up-to-date pricing, enhance driver dispatching, and fight fraud on our platform. Such solutions can process data at a massive scale in real time with exactly-once semantics,
Building Uber’s Go Monorepo with Bazel
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more In traditional industries such as automobile or aerospace, engineers first design the products and the manufacturing facilities produce the cars or aircrafts according to the design. In software development, a build system is similar to the manufacturing facilities that
Announcing a New Framework for Designing Optimal Experiments with Pyro
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Experimentation is one of humanity’s principal tools for learning about our complex world. Advances in knowledge from medicine to psychology require a rigorous, iterative process in which we formulate hypotheses and test them by collecting and analyzing new evidence. At
Uber’s Data Platform in 2019: Transforming Information to Intelligence
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber’s busy 2019 included our billionth delivery of an Uber Eats order, 24 million miles covered by bike and scooter riders on our platform, and trips to top destinations such as the Empire State Building, the Eiffel Tower, and the Golden Gate Bridge. Behind the scenes
How Uber Leverages Applied Behavioral Science at Scale
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more On the Uber Labs team, our mission is to leverage insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for customers. Our group of scientists have PhDs in fields including psychology, marketing, and cognitive
Engineering Blog | Uber Blog
You’re seeing information for US . To see local features and services for another location, select a different city. Show more Abstract Giant monolithic source-code repositories are one of the fundamental pillars of the back end infrastructure in large and fast-paced software companies. The sheer volume of everyday code changes demands a reliable and efficient change management system with three u
Meet Horovod: Uber's Open Source Distributed Deep Learning Framework for TensorFlow
Data / ML, EngineeringMeet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlowOctober 17, 2017 / Global Over the past few years, advances in deep learning have driven tremendous progress in image processing, speech recognition, and forecasting. At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep l
Designing Schemaless, Uber Engineering's Scalable Datastore Using MySQL
While all three systems are able to scale linearly by adding new nodes online, only a couple systems can also receive writes during failover. None of the solutions have a built-in way of notifying downstream dependencies of changes, so we would need to implement that at the application level. They all have indexes, but if you’re going to index many different values, the queries become slow, as the
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop Connecting users worldwide on our platform all day, every day requires an enormous amount of data management. When you consider the hundreds of operations and data science teams analyzing large sets of anonymous, aggregated data, using a variety of different tools to better understand and maintain
Using Causal Inference to Improve the Uber User Experience
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more This article is the second in our series dedicated to highlighting causal inference methods and their industry applications. Previously, we published an article on mediation modeling, which is one of many methods within the broader category of causal inference. In futur
DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber's Apache Hadoop Data Lake
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Data / MLDBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data LakeMarch 14, 2019 / Global Keeping the Uber platform reliable and real-time across our global markets is a 24/7 business. People may be going to sleep in San Franc
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Engineering Blog | Uber Blog
Turbocharging Analytics at Uber with our Data Science Workbench