Iceberg is the standard. Snowflake, Databricks, AWS, and every major query engine read and write it natively. The format question is settled.
The performance question is not.
Most production Iceberg tables are slower than they need to be — not because of anything wrong with the format, but because the physical state of the table has degraded over time. Thousands of small files from streaming writes. Manifests fragmented across hundreds of snapshots. Data scattered randomly across files with no correlation to how it's actually queried. Delete files piling up from CDC pipelines.
The result: engines scan 5–10x more data than necessary. Query planning takes seconds instead of milliseconds. S3 API costs spike. And every engine — Trino, Spark, Snowflake, Athena, DuckDB — is equally affected because they all read the same physical data layout.
This guide covers what determines Iceberg query performance, how to fix it, and the two paths available: intelligent continuous optimization through a control plane, and the manual approach with SQL and cron. Both paths target the same physical levers — the difference is whether they adapt over time or require ongoing human attention.
