In the context of Databricks, what is primarily improved by caching intermediate data?

Caching intermediate data primarily improves query performance and processing speed in Databricks. When data is cached, it is stored in memory, which allows for faster access during subsequent operations. This is particularly beneficial when dealing with large datasets or complex computations that would otherwise require repeated reading from disk storage, which is significantly slower.

By utilizing caching, you can reduce the amount of time it takes to execute queries as the data is readily available in memory, leading to a more efficient data processing workflow. This improvement is critical in iterative algorithms or when multiple queries are executed on the same data, where each query can take advantage of the cached results.

The other options—data security, data visualization, and data storage capabilities—do not directly relate to the primary purpose of caching in this context. While caching does not inherently enhance data security, visualization, or storage, its focus is specifically on optimizing performance, making it the correct choice.