Performance and Apache Iceberg's Metadata

This article is Part 3 of a 15-part Apache Iceberg Masterclass, focusing on how query engines leverage Iceberg's metadata to avoid reading unnecessary data. It details the four-stage scan planning pipeline: snapshot resolution, manifest list pruning, manifest file pruning, and Parquet internal pruning. The key performance advantage is metadata-driven data skipping, which eliminates 90-99% of files before scanning, allowing Iceberg tables with billions of rows to return results in seconds. The article also covers statistics effectiveness, sort order, file size, and caching, making it a technical deep dive for developers and data engineers.