You need to query a dataset in Amazon S3 using Amazon Athena. The dataset contains over 800,000 CSV records; each record has 200 columns and is about 1.5 MB in size, but most queries access only 5–10 columns. Which format conversion will minimize query runtime?

Converting the dataset to Apache Parquet columnar format will minimize query runtime. Parquet is a columnar storage format, meaning it stores data column by column. This is highly efficient for queries that access only a subset of columns, as Athena can read only the necessary columns from S3, significantly reducing I/O and improving performance. Given that most queries access only 5-10 out of 200 columns, Parquet's columnar nature is a perfect fit. JSON and XML are row-based formats and would require Athena to read entire records, including unneeded columns, leading to slower queries. While GZIP compression helps reduce storage and transfer size, it doesn't fundamentally change the row-based nature of CSV, so Athena would still need to decompress and process full rows.