A data engineer must load structured .csv files (15 columns) into an Amazon S3 data lake. Analysts run Amazon Athena queries that typically reference only one or two columns and seldom scan the entire file. Which approach is the most cost-effective?

The most cost-effective approach is to convert the data to Apache Parquet format. Parquet is a columnar storage format that is highly optimized for analytical queries. When analysts query only one or two columns, Athena can efficiently read only the necessary columns from Parquet files, significantly reducing the amount of data scanned and thus lowering costs. Parquet also supports compression, further reducing storage and scan costs. Using .csv or JSON format would be less cost-effective because they are row-oriented formats. Athena would have to scan entire rows, including unneeded columns, leading to higher data scan charges. Apache Avro is a row-oriented format, similar to JSON, and would not provide the same cost benefits for columnar queries as Parquet.