A company is building an analytical solution that includes Amazon S3 as data lake storage and Amazon Redshift for data warehousing. The company wants to use Amazon Redshift Spectrum to query the…

Gzip compression is not splittable, meaning Redshift Spectrum cannot process parts of a gzipped file in parallel, making 1-5 GB file sizes inefficient for large datasets.

Using a columnar storage file format, such as Apache Parquet or ORC, significantly improves Redshift Spectrum performance by allowing it to read only the specific columns required by the query, reducing I/O and data scanned from S3.

Partitioning data based on common query predicates enables Redshift Spectrum to prune S3 objects, scanning only relevant data partitions and drastically reducing the total data processed and improving query speed.

While generally advocating for larger file sizes, splitting data into finer-grained (e.g., KB-sized internal blocks within larger columnar files or highly granular files for specific use cases) can sometimes optimize parallel processing by allowing more concurrent read operations across many small data segments, provided the overhead of many small files is managed by the underlying system effectively.

Keeping files about the same size is generally a good practice for consistent parallelism, but it is not as fundamental to performance as columnar formats, partitioning, or optimal data granularity, and other choices provide more direct performance improvements.

Using file formats that are not splittable prevents Redshift Spectrum from leveraging parallel processing efficiently, leading to slower query times for large files.