charlesprabhu
07-07-2025, 05:45 AM
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.
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.