Uber's Maps Collection and Reporting (MapCARs) team shares best practices when choosing which HDFS file formats are optimal for use with Apache Spark.
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.
In 2016, Uber Engineering built and open sourced RAVE, a data model validation framework for Android that leverages Java annotation processing to protect against crashes caused by invalid data.
How Uber Engineering re-architected the content delivery feed and backend ecosystem of our new driver app to deliver an enhanced user experience.
Uber Engineering built a custom stack that generates AutoValue models using immutable collections to stably migrate Android apps at scale.
Uber Engineering explains why and how we built Chaperone, our in-house auditing system for monitoring Kafka pipeline health.
A behind-the-scenes look at how Uber Engineering continues to develop our virtual onboarding funnel which enables hundreds of thousands of driver-partners to get on the road and start earning money with Uber.
How Uber Engineering architected ride policies for Uber for Business, our way of verifying rides in real time.
Imagine you have to store data whose massive influx increases by the hour. Your first priority, after making sure you can easily add storage capacity, is to try and reduce the data’s footprint to save space. But how? This is the story of Uber Engineering’s comprehensive encoding protocol and compression algorithm test and how this discipline saved space in our Schemaless datastores.