Working with Keyed State in Flink | Flink with Java скачать в хорошем качестве

Working with Keyed State in Flink | Flink with Java

► TRY THIS YOURSELF: https://cnfl.io/flink-java-apps-module-1 When working with infinite streams of data, some operations require us to split the stream into more manageable chunks. This is done using time windows and it allows us to perform aggregations that would otherwise be impossible. When working with windows, Flink uses something known as a watermark to track time throughout the stream. In this video, you will learn the basic types of windows that can be applied to a stream, as well as how to enable watermarks. For a complete IMMERSIVE HANDS-ON EXPERIENCE: https://cnfl.io/flink-java-apps-module-1 -- ABOUT CONFLUENT Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io. #flink #java #streamprocessing #confluent