Which of the following Big Data processing models is based on the concept of continuous data flow processing?

Stream processing involves continuously analyzing data as it arrives, which is ideal for real-time applications. It processes data in real-time, as opposed to waiting for a complete dataset, making it highly efficient for scenarios requiring immediate insights, such as fraud detection, social media analytics, and sensor data analysis. Stream processing frameworks include Apache Storm, Flink, and Spark Streaming. Batch Processing: Involves processing data in large chunks rather than continuously. It is not real-time. MapReduce: A programming model used for batch processing large datasets, not real-time processing. Hadoop: A framework that supports batch processing but not real-time stream processing. HDFS: The Hadoop Distributed File System (HDFS) is for storing large data sets, not processing them.