Introduction to Data Stream Processing: Concepts and Applications

This lecture introduces the fundamental concepts of data stream processing, focusing on the integration of Apache Kafka and Spark Streaming. The instructor discusses the importance of understanding event time versus processing time, highlighting the challenges posed by delays and out-of-order data. Key concepts such as watermarks and windowing are explained, demonstrating how they help manage data streams effectively. The lecture also covers various operations on streaming data, including joins between static and dynamic datasets, and emphasizes the significance of quality assurance in project implementations. Students are guided on how to structure their final projects, including recommendations for video presentations and coding practices. The instructor stresses the need for clarity and conciseness in presentations, as well as the importance of teamwork and problem decomposition. Practical examples, such as ad monetization, illustrate the application of these concepts in real-world scenarios, preparing students for their final project submissions.