Data processing has become crucial in modern business operations due to the rapid growth of data, with batch processing and stream processing being the two primary methods utilized. Batch processing involves processing large volumes of pre-stored data through a series of steps like data ingestion, transformation, and storage, making it cost-effective and scalable for complex tasks, though it has high latency and lacks real-time interaction. In contrast, stream processing handles data in real-time as it is generated, offering low latency and immediacy, which is beneficial for time-sensitive applications such as fraud detection but can be more expensive and require more resources. The choice between these methods depends on the application's need for real-time insights, data volume and velocity, and cost considerations. While batch processing has been long established, stream processing technology is rapidly evolving, with platforms like DeltaStream offering scalable, serverless solutions for real-time data management.