Facebook can move fast and iterate because of its ability to make data-driven decisions. Data from its stream processing systems provides real-time analytics and insights; the system is also implemented into various Facebook products, which have to aggregate data from many sources. In this talk, Rajesh Nishtala covers the difficulties of stream processing at scale, the solutions Facebook has created to date, and three case studies on improving the time-to-deliver insights with data via stream processing. The case studies include examples from search product development, accelerating daily pipelines in the data warehouse, and seamless integration with machine learning platforms. Each case study shows how Facebook can deliver value to more teams while continuing to abstract the details of stream processing from various teams. Rajesh concludes by speaking to the future of stream processing.