The field of astronomy is rapidly changing away from the traditional notion of a lone astronomer pointing a telescope at a single object in a static sky. Initiatives such as the Sloan Digital Sky Survey have ushered in a collaborative big data era of wide-field sky surveys, in which telescopes collect observations continuously while sweeping across the visible night sky. This method of data collection enables not only very deep imaging of far and faint objects but is also optimal for searching for objects that might be changing or moving. By analyzing the differences in astronomical image data from one night to the next, astronomers can detect “transient” objects, such as variable stars, supernova, and near Earth asteroids. New sky surveys provide a wealth of scientific value for astronomers but not without technical challenges. Survey data need to be automatically processed and the results immediately distributed to the scientific community in order to enable rapid follow-up observations as transient astronomy can be highly time sensitive. Detection alert data distribution mechanisms need to be robust and reliable to maintain scientific integrity without data loss. Additionally, alerting systems need to be scalable to support a data volume unprecedented in astronomy, as transient detection rates have increased to exceed all historical data in a single night. A streaming architecture is an ideal architecture for automated distribution and processing of transient data in real time as it is being collected. In this talk, we will discuss how Kafka and Avro are being used in wide-field astronomical sky survey pipelines to serialize and distribute transient data, the design choices behind this system, and how this alert stream system has been successfully deployed in production to distribute transient detection alerts to the scientific research community in excess of 1 million events per night.