In the world of messaging systems, queues and streaming messages are two different concepts that serve distinct purposes. Queue-based processing is ideal for scenarios where parallel processing is needed, such as inventory management or healthcare management systems. Message queues ensure coordination among consumers, simplify coordination, and enable disjoint sets of messages to be processed independently. In contrast, streaming messages are designed for continuous flow and real-time processing, enabling applications like real-time analytics, monitoring, and machine learning. Streaming messages are typically used in scenarios where parallel processing is not necessary, such as stock prices or customer service. Apache Kafka has been the de facto standard for streaming, but its hybrid model now includes queue support, making it a one-stop solution for both streaming and queue processing. This flexibility reduces complexity, allowing businesses to meet diverse data processing needs without being locked into multiple systems. With queue support, Kafka becomes more versatile, providing reliability, scalability, and performance that organizations need to thrive in an increasingly data-driven world.