What is caching used for in Databricks?

Caching in Databricks is primarily used to speed up queries and data processing. When data is cached, it is stored in memory (RAM) rather than being fetched repeatedly from disk. This allows for faster access to data during subsequent operations. By retaining frequently accessed data in memory, caching reduces the latency associated with disk I/O operations, making processes like querying and transformations significantly quicker.

This is particularly beneficial in environments where the same data is accessed multiple times, as it minimizes the need for compute resources to repeatedly load the same datasets, thereby optimizing performance and resource allocation. Furthermore, caching is especially useful when working with large datasets in iterative algorithms, as it can drastically reduce the overall computation time.

In contrast, options involving permanent data storage or machine learning model creation do not accurately represent the primary function of caching within Databricks. While executing background tasks might benefit from cached data, it does not define the primary purpose of caching in the context of enhancing performance and efficiency in data queries and processing.