A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket.

[Reference: Amazon Redshift Spectrum and Columnar File Formats, C. Partition the order data in the S3 bucket based on order date:, Partitioning the data on columns like order date allows Redshift Spectrum to skip scanning unnecessary partitions, leading to improved query performance., By organizing data into partitions, you minimize the number of files Spectrum has to read, further optimizing performance., Reference: Best Practices for Amazon Redshift Spectrum Performance, Alternatives Considered:, B (Develop an AWS Glue ETL job): While consolidating files can improve performance by reducing the number of small files (which can be inefficient to process), it adds additional ETL complexity. Switching to a columnar format (Option A) and partitioning (Option C) provides more significant performance improvements with less development effort., D and E (JSON-related options): Using JSON format or the SUPER type in Redshift introduces complexity and isn't as efficient as the proposed solutions, especially since JSON is not a columnar format., References:, Amazon Redshift Spectrum Documentation, Columnar Formats and Data Partitioning in S3, , ]