How do you differentiate between caching and partitioning strategies in Databricks, and how do you decide which feature to use for performance optimization?

Caching in Databricks stores a DataFrame or RDD in memory (or on disk if memory is insufficient) so that subsequent actions can read the data directly from the cache instead of recomputing or reading from storage. Partitioning, on the other hand, reorganizes the underlying data files on disk by a key or set of keys, which reduces shuffle during filter and join operations because data that belongs together is co‑located. You choose caching when the same dataset is reused many times in a session or when you have a small to medium sized dataset that fits comfortably in cluster memory. Partitioning is preferred for very large tables that are read once or infrequently, especially when queries filter on the partition key; it also helps with data pruning and reduces the amount of data read from storage. In practice, I first profile the workload: if the same table is scanned multiple times, I cache it; if the table is huge and queries filter on a column, I repartition or add a partition column. I also monitor memory usage and GC overhead to avoid spilling when caching, and I use the repartition or coalesce functions to control partition size for optimal shuffle performance.