Stream processing frameworks are software tools designed to handle and analyze continuous streams of data in real-time. They allow organizations to process large volumes of data as it arrives, enabling quick decision-making and immediate insights. Common use cases include monitoring social media feeds, financial transactions, and sensor data from IoT devices. These frameworks typically support various programming languages and provide features like fault tolerance, scalability, and event time processing. Popular examples include Apache Kafka, Apache Flink, and Apache Storm. By using these frameworks, businesses can efficiently manage data flows and respond to events as they happen.