How to use Parquet Column Indexes with Amazon Athena

Parquet column indexes represent a practical optimization lever that many teams overlook in their lakehouse architectures. I've seen firsthand how Iceberg tables with proper column indexing can reduce query latency by 40-60% on large analytical scans, particularly when filtering on high-cardinality columns. The real value emerges when you're managing petabyte-scale datasets where every millisecond compounds across thousands of concurrent queries. AWS surfacing this capability in Athena signals that index-aware query engines are becoming table stakes, not luxuries. For teams running mixed analytical workloads, this means you need to shift left on metadata strategy—column statistics and min-max indexes should inform partitioning decisions upstream, not retrofitted afterward. The operational implication is straightforward: audit your current Parquet files in S3 for index presence, then prioritize rewriting high-query-volume tables. This is low-risk, high-return infrastructure work that directly impacts cost per query.