Think Different

  Photo by João Jesus: pexel A common goal of stream processing is to aggregate events into temporal intervals, or windows. For example, to count the number of social media posts per minute or to calculate the average rainfall per hour. Azure Stream Analytics includes native support for five kinds of temporal windowing functions. These functions enable you to define temporal intervals into which data is aggregated in a query. The supported windowing functions are Tumbling, Hopping, Sliding, Session, and Snapshot. No, these windowing functions are not exclusive to Azure Stream Analytics. They are commonly used concepts in stream processing and are available in various stream processing frameworks and platforms beyond Azure, such as Apache Flink, Apache Kafka Streams, and Apache Spark Streaming. The syntax and implementation might vary slightly between different platforms, but the underlying concepts remain the same. Five different types of Window functions Tumbling Window (Azure St...