Uber is making rapid strides as a thriving Marketplace/Logistics platform. The Marketplace system consists of a set of services that are responsible for handling rider requests and other fulfillment requests from UberEats, Rush etc. To make our Marketplace system efficient and intelligent, we need to extract deep and timely insights from our carefully curated data. We also have to make the insights easily accessible for both people (Operations, Data Scientists and Engineers) and Machines to consume in real time.

This talk will discuss how stream processing is used within the Uber's Marketplace system to solve a wide range problems, including but not limited to realtime indexing and querying of geospatial time series, aggregation and computing of streaming data, and extracting patterns from data streams. In addition, it will touch upon various TimeSeries analysis and predictions. The underlying systems utilize many open source technologies such as Apache Kafka, Samza and Spark streaming.