Using Apache Iceberg in Cloudera Data Engineering
Cloudera Data Engineering (CDE) supports Apache Iceberg which provides a table format
for huge analytic datasets in the cloud. 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 Cloudera Data Engineering virtual clusters running Spark 3 to interact with Apache
Iceberg tables.
Prerequisites and limitations for using Iceberg Accessing Iceberg tables Creating Virtual Cluster with Spark 3 Creating and running Spark 3.2.1 Iceberg jobs 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 