Using Apache Iceberg in CDS
CDS supports Apache Iceberg which provides a table format for huge analytic datasets.
Iceberg enables you to work with large tables, especially on object stores, and supports
concurrent reads and writes on all storage media. You can use CDS running Spark 3 to interact
with Apache Iceberg tables.
Prerequisites and limitations for using Iceberg in Spark Accessing Iceberg tables Creating a new Iceberg table from Spark 3 Configuring Hive Metastore for Iceberg column changes Importing and migrating Iceberg table in Spark 3 Importing and migrating Iceberg table format v2 Configuring Catalog Loading data into an unpartitioned table Querying data in an Iceberg table Updating Iceberg table data Iceberg library dependencies for Spark applications 