Which type of processing is categorized as a common Big Data workload?

In the context of Big Data workloads, in-memory processing is categorized as a common type of processing because it involves storing data in a computer's main memory (RAM) for fast access, which significantly accelerates data processing speeds compared to traditional disk-based systems. This is particularly beneficial for applications that require quick data retrieval and transformation, such as real-time analytics and decision-making.

In-memory processing enables organizations to handle large volumes of data efficiently, allowing for rapid computations and the ability to work with highly dynamic datasets. It supports scenarios where high throughput and low latency are essential, making it a prevalent choice in environments that deal with extensive data analysis and real-time insights.

Additionally, it facilitates more complex queries and processing tasks that benefit from speed, positioning it as a crucial element within the Big Data landscape. Organizations often combine in-memory processing with other methodologies to leverage the benefits of both speed and data capacity effectively.