How does batch processing differ from stream processing?

Batch processing is characterized by its ability to handle large volumes of data at once, processing these datasets in discrete groups or "batches." This method is often utilized in scenarios where immediate data availability is not crucial, such as end-of-day reporting or periodic aggregations. By processing data in batches, it can efficiently manage large data sets, allowing for various operations to be performed simultaneously on the entire dataset.

In contrast, stream processing, also known as real-time data processing, is designed to handle data as it is created or received. This approach allows organizations to analyze and react to data in real-time, which is critical for applications requiring immediate insights, such as fraud detection or monitoring systems. Stream processing handles data incrementally, enabling continuous computation over data that arrives continually over time.

This differentiation between the two processing methods highlights why batch processing is defined by its ability to work with large volumes of data all at once, while stream processing is focused on continuous real-time data processing and analysis.