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Uber Data

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DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data Lake

Uber engineers discuss the development of DBEvents, a change data capture system designed for high data quality and freshness that is capable of operating on a global scale.

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.
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Why Financial Planning is Exciting… At Least for a Data Scientist

In this article, Uber’s Marianne Borzic Ducournau discusses why financial planning at Uber presents unique and challenging opportunities for data scientists.

How Uber Leverages Applied Behavioral Science at Scale

Uber Labs utilizes insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for users on our platform.

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.

Women in Data Science at Uber: Moving the World With Data

During an October 2018 meetup, members of our Women in Statistics, Data, Optimization, and Machine Learning (WiSDOM) group presented on their technical work at Uber.

Four Ways Uber Visualization Made an Impact in 2018

Uber's Head of Urban Computing & Visualization reflects on his team's work visualizing data to better understand urban mobility in 2018—and beyond.
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Sessionizing Uber Trips in Real Time

Uber's many data flows required modeling the data associated with a specific task, such as a rider trip, into a state machine. The state machine lets engineers focus on just the events needed to successfully accomplish a trip.

Analyzing Experiment Outcomes: Beyond Average Treatment Effects

Quantile treatment effects (QTEs) enable our data scientists to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the Uber marketplace.

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.

Uber Expands Advanced Visualization Ecosystem with Mapbox Integration

Uber Visualization announces partnership with Mapbox to enhance our data visualization tools and grow our open source community.
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Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache...

Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
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Talking Safety with Uber Data Scientist Sunny Jeon

In an interview for the Uber Eng blog, Data Scientist Sunny Jeon talks about how his team develops solutions in order to advance Uber's core value of safety.

Forecasting at Uber: An Introduction

In this article, we provide a general overview of how our teams leverage forecasting to build better products and maintain the health of the Uber marketplace.

Under the Hood of Uber’s Experimentation Platform

Uber's experimentation platform empowers us to improve the customer experience by allowing teams to launch, debug, measure, and monitor product changes.

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.

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.

Herb: Multi-DC Replication Engine for Uber’s Schemaless Datastore

Facing the need for a resilient data structure over thousands of storage nodes to serve the 15 million rides per day that occur on our platform, Uber engineers developed Herb, our data replication solution. Herb ensures data availability and integrity across our data centers.

M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model

With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
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Food Discovery with Uber Eats: Building a Query Understanding Engine

Uber engineers share how we process search terms for our Uber Eats service, using query understanding and expansion to find restaurants and menu items that best match what our eaters want.

From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox

Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.

Growing the Data Visualization Community with deck.gl v5

deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.

Mediation Modeling at Uber: Understanding Why Product Changes Work (and Don’t Work)

Uber Labs leverages mediation modeling to better understand the relationship between product updates and their outcomes, leading to improved customer experiences on our platform.

Queryparser, an Open Source Tool for Parsing and Analyzing SQL

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.

Implementing Model-Agnosticism in Uber’s Real-Time Anomaly Detection Platform

Uber Engineering extended our anomaly detection platform's ability to integrate new forecast models, allowing this critical on-call service to scale to meet more complex use cases.

Welcoming the Era of Deep Neuroevolution

By leveraging neuroevolution to train deep neural networks, Uber AI Labs is developing solutions to solve reinforcement learning problems.

Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning

Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.

Engineering More Reliable Transportation with Machine Learning and AI at Uber

In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.

Turbocharging Analytics at Uber with our Data Science Workbench

Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.

Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow

Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.

Engineering Restaurant Manager, our UberEATS Analytics Dashboard

The UberEATS Restaurant Manager gives restaurant partners insight into their business by measuring customer satisfaction, sales, and service quality.

Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

Uber Engineering introduces a new Bayesian neural network architecture that more accurately forecasts time series predictions and uncertainty estimations.

Meet Michelangelo: Uber’s Machine Learning Platform

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’s Ride with the Sun: Tracking the 2017 Solar Eclipse

Uber Engineering’s Data Visualization team uses their deck.gl and Voyager visualization platforms to map rider behavior during the August 21, 2017 solar eclipse.

Engineering Uber’s Self-Driving Car Visualization Platform for the Web

Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.

Engineering Data Analytics with Presto and Apache Parquet at Uber

Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.

Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.

Building an Intelligent Experimentation Platform with Uber Engineering

Composed of a staged rollout and intelligent analytics tool, Uber Engineering's experimentation platform is capable of stably deploying new features at scale across our apps. In this article, we discuss the challenges and opportunities we faced when building this product.

Open Sourcing deck.gl 4.0: Uber Engineering’s Framework for Advanced Data Visualization

Uber Engineering debuts deck.gl 4.0, the latest version of our open source data visualization framework featuring enhanced geospatial exploration, a re-architected codebase, and more comprehensive documentation.

How Uber Engineering Increases Safe Driving with Telematics

The engineering behind how Uber's Driving Safety team is using telematics to raise awareness of driving patterns to our partners.

Engineering Intelligence Through Data Visualization at Uber

The data visualization team in Uber Engineering delivers intelligence through crafting visual exploratory data analysis tools. Here's what some of these visualizations look like.

Streamific, the Ingestion Service for Hadoop Big Data at Uber Engineering

Here we look at Hadoop data ingestion, and how Uber Engineering streams diverse data into a cohesive layer for querying in near real-time using our in-house developed Streamific.

Identifying Outages with Argos, Uber Engineering’s Real-Time Monitoring and Root-Cause Exploration Tool

The story of Argos: How Uber Engineering provides highly accurate, real-time alerts on millions of system and business metrics in Uber's fast-paced environment.

ETA Phone Home: How Uber Engineers an Efficient Route

How the Uber Map Services Team thought about building our most advanced Routing Engine to date, improving ETAs and powering products like uberPOOL.

Engineer Q&A: Doing Data Science at Uber Engineering

This week, Emi Wang dishes out data knowledge on what she’s been up to at Uber since she joined in September 2012.

The Pulse of a City: How People Move Using Uber Engineering

When do people most frequently request rides? Using Uber Data in 2014, we see how cities around the world have different rhythms of movement. Here's the pulse of New York City, London, Los Angeles, San Francisco, Chicago and Miami.

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