When reading a CSV file and saving as a Delta table, which option helps in optimizing the read operation?

When saving a CSV file as a Delta table, optimizing read operations is crucial for performance, and one effective strategy is to remove unnecessary columns. This practice streamlines the data stored in the Delta table, which can reduce the amount of data that needs to be read during queries.

By eliminating columns that are not required for specific analyses or operations, you minimize the data footprint, leading to faster read times. This also benefits storage efficiency, as less data means a reduced amount of disk space is used. Consequently, when queries are executed, the system can process the smaller dataset more swiftly, improving overall performance.

While inferring the schema, partitioning, and explicitly defining the schema can all have benefits in different contexts, they do not directly contribute to reducing the amount of data processed in the same way that removing unnecessary columns does. Hence, focusing on the relevant data ensures that the read operation is optimized for speed and efficiency.