Which other processing method is widely considered common in Big Data workloads?

Batch processing is widely considered common in Big Data workloads due to its ability to handle large volumes of data efficiently. This processing method involves collecting and storing raw data over a period, and then processing it all at once. This is particularly beneficial for generating reports, data analysis, and machine learning tasks, where data needs to be aggregated and analyzed comprehensively to derive meaningful insights.

Batch processing excels in scenarios where real-time processing is not critical, allowing for more resource-efficient computations. It is often used in environments where large datasets are processed on a regular schedule, such as daily or weekly, making it a foundational approach in Big Data frameworks.

Other methods, while useful for specific scenarios, do not match the broad applicability and established infrastructure that supports batch processing across diverse industries dealing with large datasets. Therefore, its prominence in the realm of Big Data workloads makes it a standard choice for many applications, especially when dealing with historical data or heavy computational tasks.