A user is preparing to load data from an external stage.
Which practice will provide the MOST efficient loading performance?
A user is preparing to load data from an external stage.
Which practice will provide the MOST efficient loading performance?
Organizing files into logical paths provides the most efficient loading performance. This approach allows for the partitioning of data, which can then be loaded in parallel using concurrent COPY commands, significantly improving the overall performance. Loading the data in one large file is less efficient due to limited parallel processing capabilities, whereas using logical paths takes full advantage of parallel operations.
Both internal (i.e. Snowflake) and external (Amazon S3, Google Cloud Storage, or Microsoft Azure) stage references can include a path (or prefix in AWS terminology). When staging regular data sets, we recommend partitioning the data into logical paths that include identifying details such as geographical location or other source identifiers, along with the date when the data was written. Organizing your data files by path lets you copy any fraction of the partitioned data into Snowflake with a single command. This allows you to execute concurrent COPY statements that match a subset of files, taking advantage of parallel operations. https://docs.snowflake.com/en/user-guide/data-load-considerations-stage.html#organizing-data-by-path
https://docs.snowflake.com/en/user-guide/data-load-considerations-stage.html#organizing-data-by-path
The answer is A
The correct answer is A
correct