Prerequisites and limitations
Learn about the supported versions for Cloudera Data Engineering (CDE), Spark, and Data Lake to use with Apache Iceberg CDE.
To use Apache Iceberg in CDE, you'll need the following prerequisites:
- Spark 3.2 or higher
- A compatible version of Data Lake as listed in CDE and Data Lake compatibility linked below
- CDE 1.16 or higher
- AWS or Azure is supported starting in CDE 1.17-h1 (which supports Iceberg 0.14)
Limitations
- The use of Iceberg tables as Structured Streaming sources or sinks is not supported.
- PyIceberg is not supported. Using Spark SQL to query Iceberg tables in PySpark is supported.
Iceberg table format version 2
Iceberg table format version 2 (v2) is available starting in Iceberg 0.14. Iceberg table format v2 uses row-level UPDATE and DELETE operations that add deleted files to encoded rows that were deleted from existing data files. The DELETE, UPDATE, and MERGE operations function by writing delete files instead of rewriting the affected data files. Additionally, upon reading the data, the encoded deletes are applied to the affected rows that are read. This functionality is called merge-on-read.
To use Iceberg table format v2, you'll need the following prerequisites:
- CDE 1.17-h1 or higher
- Iceberg 0.14
- Spark 3.2 or higher
