Introduction to Data Stream Processing

This lecture introduces data stream processing, focusing on the concepts, tools, and challenges involved. It covers the difference between batch and stream processing, the importance of stream processing for real-time insights, and applications in various industries such as log analysis, fraud detection, and predictive maintenance. The lecture also discusses related concepts like event time vs processing time, window operations, and stateful vs stateless operations. Tools like Kafka and Spark Streaming are explored, along with practical exercises on setting up Kafka, producing and consuming messages, and visualizing data streams.