A Data Engineer is working on a continuous data pipeline which receives data from Amazon...

This option is the best way to ensure that Snowpipe is performant while minimizing costs. By splitting the files before loading them, the Data Engineer can reduce the size of each file and increase the parallelism of loading. By setting the SIZE_LIMIT option to 250 MB, the Data Engineer can specify the maximum file size that can be loaded bySnowpipe, which can prevent performance degradation or errors due to large files. The other options are not optimal because:

Increasing the size of the virtual warehouse used by Snowpipe will increase the performance but also increase the costs, as larger warehouses consume more credits per hour.

Changing the file compression size and increasing the frequency of the Snowpipe loads will not have much impact on performance or costs, as Snowpipe already supports various compression formats and automatically loads files as soon as they are detected in the stage.

Decreasing the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose will not affect Snowpipe performance or costs, as Snowpipe does not depend on Kinesis Firehose buffer size but rather on its own SIZE_LIMIT option.