3 Iceberg partitioning best practices to improve performance

The blog post explores the advantages and implementation of partitioning strategies in data lake tables, specifically using Apache Iceberg within Trino, to enhance query performance and scalability. It illustrates the concept of partitioning as a method to organize data into subdirectories, allowing queries to target specific subsets of data, thus reducing resource consumption and improving efficiency. The post emphasizes the importance of selecting an efficient partitioning strategy, particularly for large tables, and highlights Apache Iceberg's unique features like hidden partitioning and partition evolution, which allow dynamic adjustments without rewriting data. It also discusses the challenges of small file sizes in query performance and advocates for the use of compaction tools offered by modern table formats to address this issue. The text concludes by positioning Apache Iceberg as an optimal table format due to its advanced partitioning features and encourages the use of an "Icehouse" setup, combining Trino and Iceberg, for optimal data management and query performance in cloud data environments.