This is Part 3 of a 15-part Apache Iceberg Masterclass . Part 2 covered the metadata structures of all five table formats. This article focuses on exactly how query engines use Iceberg's metadata to avoid reading data they don't need. The single biggest performance advantage of Iceberg over raw data lakes is not a clever algorithm or a faster codec. It is metadata-driven data skipping. By the time a query engine begins scanning actual Parquet files, Iceberg's metadata has already eliminated 90-99% of the files from consideration. Understanding this process explains why Iceberg tables with billions of rows can return query results in seconds. Table of Contents What Are Table Formats and Why Were They Needed? The Metadata Structure of Current Table Formats Performance and Apache Iceberg's Metadata Technical Deep Dive on Partition Evolution Technical Deep Dive on Hidden Partitioning Writing to an Apache Iceberg Table What Are Lakehouse Catalogs?…