Uber's business is highly real-time in nature. PBs of data is continuously being collected from the end users such as Uber drivers, riders, restaurants, eaters and so on everyday. There is a lot of valuable information to be processed and many decisions must be made in seconds for a variety of use cases such as customer incentives, fraud detection, machine learning model prediction. In addition, there is an increasing need to expose this ability to different user categories, including engineers, data scientists, executives and operations personnel which adds to the complexity. In this paper, we present the overall architecture of the real-time data infrastructure and identify three scaling challenges that we need to continuously address for each component in the architecture. At Uber, we heavily rely on open source technologies for the key areas of the infrastructure. On top of those open-source software, we add significant improvements and customizations to make the open-source solutions fit in Uber's environment and bridge the gaps to meet Uber's unique scale and requirements. We then highlight several important use cases and show their real-time solutions and tradeoffs. Finally, we reflect on the lessons we learned as we built, operated and scaled these systems.

Linked In is among the largest social networking sites in the world. As the company has grown, our core data sets and request processing requirements have grown as well. In this paper, we describe a few selected data infrastructure projects at Linked In that have helped us accommodate this increasing scale. Most of those projects build on existing open source projects and are themselves available as open source. The projects covered in this paper include: (1) Voldemort: a scalable and fault tolerant key-value store, (2) Data bus: a framework for delivering database changes to downstream applications, (3) Espresso: a distributed data store that supports flexible schemas and secondary indexing, (4) Kafka: a scalable and efficient messaging system for collecting various user activity events and log data.

We use modern features of web browsers to develop a secure login system from an untrusted terminal. The system, called Session Juggler, requires no server-side changes and no special software on the terminal beyond a modern web browser. This important property makes adoption much easier than with previous proposals. With Session Juggler users never enter their long term credential on the untrusted terminal. Instead, users log in to a web site using a smartphone app and then transfer the entire session, including cookies and all other session state, to the untrusted terminal. We show that Session Juggler works on all the Alexa top 100 sites except eight. Of those eight, five failures were due to the site enforcing IP session binding. We also show that Session Juggler works flawlessly with Facebook connect. Beyond login, Session Juggler also provides a secure logout mechanism where the trusted phone is used to kill the session. To validate the session juggling concept we conducted a number of web site surveys that are of independent interest. First, we survey how web sites bind a session token to a specific device and show that most use fairly basic techniques that are easily defeated. Second, we survey how web sites handle logout and show that many popular sites surprisingly do not properly handle logout requests.