A company has 10–15 TB of uncompressed .csv files in Amazon S3 and plans to run a one-time evaluation using Amazon Athena. The company wants to transform the data to reduce query times and lower storage costs. Which file format and compression choice best satisfies these requirements for Athena queries?

Apache Parquet with Snappy compression is the optimal choice for several reasons. Parquet is a columnar storage format, which means it stores data by column rather than by row. This is highly efficient for analytical queries because Athena can read only the necessary columns, significantly reducing the amount of data scanned and thus improving query performance and lowering costs. Snappy is a fast compression algorithm that offers a good balance between compression ratio and decompression speed, further enhancing query performance. CSV with ZIP is less efficient because CSV is a row-based format, requiring Athena to scan entire rows even if only a few columns are needed. ZIP compression is also generally less performant for analytical workloads compared to columnar formats with optimized compression. JSON with bzip2 suffers from similar row-based inefficiencies as CSV, and bzip2, while offering good compression, is slower for decompression than Snappy. Apache Avro is a row-based format, making it less efficient for analytical queries than columnar formats like Parquet, and LZO, while fast, doesn't overcome the fundamental inefficiency of row-based storage for this use case.