[sahithya]

Batch processing handles data in large, predefined chunks. It's ideal for historical analysis, end-of-day reports, or tasks where immediate results aren't critical. Think of it like processing a stack of invoices once a week. Data is collected over a period, then processed all at once, offering high throughput and efficient resource utilization for large datasets.

Stream processing, conversely, deals with data continuously as it's generated. This "real-time" approach is vital for applications requiring immediate insights, such as fraud detection, live dashboards, or IoT analytics. Data is processed milliseconds after creation, enabling rapid responses and proactive decision-making. While offering lower latency, it often requires more sophisticated infrastructure to manage continuous data flow. The choice depends on the application's latency and throughput requirements.