Introduction As Uber’s business grew, we scaled our Apache Hadoop (referred to as ‘Hadoop’ in this article) deployment to 21000+ hosts in 5 years, to...
Introduction The primary goal for customer support is to ensure users’ issues are addressed and resolved in a timely and effective manner. The kind of...
At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue...
Data powers Uber Uber has revolutionized how the world moves by powering billions of rides and deliveries connecting millions of riders, businesses, restaurants, drivers, and...
Every day in over 10,000 cities around the world, millions of people rely on Uber to travel, order food, and ship cargo. Our apps...
Introduction Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence...
Uber ATG's self-driving vehicles measure a multitude of possible scenario variations to answer the age-old question: "how does the pedestrian cross the road?"
Uber employs statistical modeling to find anomalies in data and continually monitor data quality.
We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.
In October 2019, Uber hosted our second annual Moving The World With Data meetup, showcasing some of our most interesting data science challenges in 2019.
We implemented a Kappa architecture at Uber to effectively backfill streaming data at scale, ensuring accurate data in our platform.
We engineered full SQL support on Apache Pinot to enable quick analysis and reporting on aggregated data, leading to improved experiences on our platform.
As part of Uber Visualization's all-team hackathon, we built SpeedsUp, a project using machine learning to process average speeds across a city, cluster the results, and overlay them on a street map.
In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
As part of Uber Visualization's all-team hackathon, we built Urban Symphony, an Uber Movement visualization that adds an audio component to traffic speed patterns.
With the release of deck.gl version 7.3, Uber’s open source visualization tool now supports rendering massive geospatial data sets formatted according to the OGC 3D Tiles community standard.
Uber has embraced Presto, a high performance, distributed SQL query engine, and joined the Presto Foundation. Meet the Uber engineers who contribute to and use Presto on a daily basis.
Suzette Puente, Uber Data Science Manager, shares how she applies her graduate work in statistics to forecast traffic patterns and generate better routes.
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.