Explores using Lance and Iceberg formats for multimodal AI data, addressing scan-heavy analytics vs. random-access retrieval for ML training.
Compares Apache Paimon and Iceberg for handling mutable streams, focusing on Paimon's LSM-tree architecture for high-frequency updates.
Explains how Apache Iceberg uses metadata for data skipping, enabling fast query performance by eliminating 90-99% of files before scanning.
Apache Polaris is an open-source catalog service that unifies the Iceberg ecosystem by implementing the Iceberg REST API for vendor-neutral lakehouse metadata management.
Explores two paths for building a universal lakehouse catalog that extends beyond Apache Iceberg tables to manage diverse data formats and sources.
A hands-on guide to using different catalogs, including Apache Hive, with Flink SQL, covering installation, configuration, and practical insights.
