An ML engineer must build a pipeline that uses Amazon Athena for two workloads: large-scale batch transforms and model training, and near-real-time low-latency queries for inference and analytics. Which file format will produce the LOWEST latency for both batch and near-real-time processing?

Apache Parquet is a columnar storage format optimized for analytical queries. Its columnar nature allows Athena to read only the necessary columns for a query, significantly reducing I/O and improving performance for both large-scale batch processing and low-latency, near-real-time queries. It also supports efficient compression and encoding schemes, further reducing data size and query execution time. CSV is a row-oriented format that requires reading entire rows, even if only a few columns are needed, leading to higher latency. Nested JSON and Deserialized JSON are also row-oriented and generally less efficient for analytical queries than columnar formats like Parquet due to their schema flexibility and often larger file sizes, which increase I/O and processing overhead.