Unlocking Real-Time Insights: Uber Freight's Evolution from Batch to Streaming Analytics

With Uber Freight overseeing a staggering billion loads annually, the demand for real-time analytics is paramount. Delve into our journey as we transitioned from traditional data aggregation methods reliant on stored procedures to a dynamic real-time application powered by Apache Kafka and Flink. Explore on how this innovative architecture revolutionized our operations, slashing data aggregation latency from fifteen minutes to mere seconds.  

   In this engaging talk, we will spotlight on two key pillars: crafting resilient streaming pipelines leveraging Apache Kafka and the implementation of agile real-time processing jobs with Apache Flink. Join us as we unveil our innovative approach to freight analytics, showcasing a compelling use case that highlights the power of real-time analytics and demonstrates our seamless transition from batch to stream processing