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Pruning for Iceberg

snowflake.com · 1,027 words · saved by 1 readers

Apache Iceberg is an open source table format designed for massive data lakes, capable of handling petabyte-scale tables. It also ensures seamless interoperability between various tools used to extract value from data lakes. Last year, Snowflake announced significant enhancements to its Apache Iceberg™ table support. Efficiently processing enormous tables requires effective data pruning — the process of skipping unnecessary data when calculating query results. In this blog post, we’ll explore how Snowflake optimizes pruning at all levels of Parquet files, the most common data format for Iceberg tables. Pruning leverages efficient metadata operations to eliminate large data chunks before they are processed or loaded. It can be applied not only to simple filter predicates such as WHERE order_date < '2025-01-10' but also to more complex queries, including Top-K queries and joins. Snowflake has consistently provided file-level pruning for Iceberg tables by default. However, because Iceberg

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