Millions of people worldwide use Uber daily, generating vast amounts of data on traffic, routes, estimated times, and more. We use this data to learn and enhance their experiences with Uber.We developed DSW (Data Science Workbench), an interactive notebook platform for applied scientists, data scientists, ML engineers, and operations specialists to facilitate this learning. DSW supports data exploration, analysis, model training, workflow scheduling, visualization, and collaboration through a web interface.

The Uber Engineering Blog offers insights, technical deep dives, and updates on the engineering challenges and solutions behind Uber's platform and services. Covering topics such as distributed systems, data infrastructure, and mobile development, the blog provides resources for developers interested in large-scale systems and real-world engineering problems. Developers can learn about Uber's technology stack, engineering culture, and best practices for building scalable and reliable systems.

Uber Engineering

Uber shares insights into migrating large-scale interactive workloads from Peloton to Kubernetes. The process involved migrating 3,500 Jupyter and RStudio sessions with minimal disruption, leveraging Kubernetes features like Jobs and Federation for improved efficiency. Challenges included managing interactive sessions, NFS support, and maintaining user-installed packages across platform transitions. This migration unlocked better resource sharing and cloud integration for the company.

Migrating Large-Scale Interactive Compute Workloads to Kubernetes Without Disruption