Stream processing is a critical component of modern data architectures, facilitating real-time analytics and event-driven applications by handling continuous data flows, such as logs or sensor readings, as they occur. This approach contrasts with traditional batch processing, with stateless and stateful stream processing representing two fundamental paradigms. Stateless stream processing handles each event independently without memory of previous events, offering simplicity, scalability, and low latency, making it suitable for straightforward transformations. In contrast, stateful stream processing retains memory across events, supporting complex computations like aggregations and pattern detection, albeit with increased resource demands and latency due to state management. The choice between stateless and stateful processing depends on the specific use case, with stateless being ideal for fast, simple operations, and stateful necessary for scenarios requiring historical context and complex analysis. Modern stream processing frameworks like Apache Kafka Streams, Apache Flink, and Spark Streaming provide the infrastructure to implement both paradigms, often supporting hybrid pipelines that leverage the strengths of each.