A data engineering team is tasked with loading a large dataset (5TB) into Snowflake from an external S3 bucket. The data loading process is experiencing significant performance bottlenecks. Which of the following strategies would MOST effectively improve the data loading performance, assuming the network bandwidth between Snowflake and S3 is sufficient?
Correct Answer: A,E
Options A and E are the most effective. Increasing warehouse size provides more compute resources for parallel loading. Partitioning the data into smaller files in S3 allows Snowflake to parallelize the load process across multiple compute nodes within the warehouse. Option B might seem useful but Snowflake inherently parallelizes loading from S3 with a single warehouse if the data is properly partitioned. Option C is not cost-effective. Option D might help avoid some overhead but is less impactful than warehouse sizing and data partitioning.