Apache Iceberg is a table format for huge analytics datasets in the cloud that defines how metadata is stored and data files are organized. Iceberg is also a library that compute engines can use to read/write a table.
- Reads of Iceberg V2 tables that have had row-level deletes or updates makes Apache Iceberg ACID-compliant with serializable isolation and an optimistic concurrency model
- Materialized views of Iceberg tables
- Enhanced maintenance features, such as expiring and removing old snapshots and compaction of small files
- Performance and scalability enhancements
Cloudera supports Iceberg in Cloudera Data Warehouse in AWS and Azure environments.
The Hive metastore stores Iceberg metadata, including the location of the table.
Hive metastore plays a lightweight role in the Catalog operations. Iceberg relieves Hive metastore (HMS) pressure by storing partition information in metadata files on the file system/object store instead of within the HMS. This architecture supports rapid scaling without performance hits.
By default, Hive and Impala use the Iceberg HiveCatalog. Cloudera recommends the default HiveCatalog to create an Iceberg table.
Apache Iceberg integrates Apache Ranger for security. You can use Ranger integration with Hive and Impala to apply fine-grained access control to sensitive data in Iceberg tables. Iceberg is also integrated with Data Visualization for creating dashboards and other graphics of your Iceberg data.