Lecture
This lecture introduces the fundamental concepts of data stream processing, emphasizing its importance in real-time data analysis. The instructor begins by contrasting batch processing with stream processing, highlighting the need for immediate insights from continuously generated data. Various applications of stream processing are discussed, including real-time monitoring, fraud detection, and predictive maintenance. The lecture covers the constraints and challenges associated with processing streaming data, such as handling unbounded, unordered, and incomplete data. The instructor explains the concept of sliding windows for managing data flow and introduces tools like Apache Kafka and Spark Streaming, which facilitate the implementation of stream processing. The session concludes with practical exercises that involve setting up Kafka, producing and consuming data streams, and applying machine learning algorithms to real-time data. This comprehensive overview equips students with the knowledge to effectively utilize stream processing in their projects.