What problem does it solve?
Databricks users need a consistent, scalable way to manage Apache Iceberg data across native Iceberg tables, Delta UniForm interoperability, and external engine access. This Skill consolidates best practices for creating and using Iceberg in Databricks, including Managed Iceberg, UniForm (External Iceberg Reads), Compatibility Mode for streaming tables and MVs, and the Iceberg REST Catalog (IRC) for external tooling such as PyIceberg, Spark, and Snowflake interop.
Core Features & Use Cases
- Native Iceberg tables (Managed Iceberg) with full read/write in Databricks and external engines
- UniForm for Delta-to-Iceberg interoperability (external reads as Iceberg; internal writes as Delta)
- Compatibility Mode for streaming tables and materialized views in SDP pipelines
- Iceberg REST Catalog (IRC) to expose Databricks data to external engines
- Snowflake interoperability via catalog integrations and foreign catalogs
- PyIceberg and OSS Spark integration for external data access
Quick Start
Use this skill to configure a Databricks Iceberg workflow choosing between Managed Iceberg, UniForm, or IRC and enabling external engine access via UC.
