To speed up Amazon Athena queries, a data engineer finds that all input files are uncompressed .csv and that most queries select a single column. Which change will MOST improve Athena query performance?

Converting data from CSV to Apache Parquet with Snappy compression will significantly improve Athena query performance. Parquet is a columnar storage format, meaning it stores data column by column. This is highly efficient for queries that select a subset of columns, as Athena only needs to read the relevant columns, reducing I/O operations. Snappy is a fast compression algorithm that balances compression ratio with decompression speed, further enhancing performance. Converting to JSON (option A) is not ideal because JSON is a row-oriented format, offering no columnar benefits. Simply compressing existing CSV files (options B and D) will reduce storage and I/O but won't provide the performance boost of columnar storage for queries selecting specific columns. Gzip (option D) offers good compression but is generally slower for decompression than Snappy, making Snappy a better choice for query performance.