databricks-iceberg

What problem does it solve?

This Skill provides comprehensive guidance and tools for working with Apache Iceberg tables within the Databricks ecosystem, enabling efficient data management and interoperability across various platforms.

Core Features & Use Cases

• Managed Iceberg Tables: Create, read, and write native Iceberg tables directly in Databricks.
• Delta-to-Iceberg Interoperability: Use UniForm to make Delta tables readable as Iceberg externally, and Compatibility Mode for streaming tables and materialized views.
• External Engine Access: Connect external tools like Snowflake, PyIceberg, and OSS Spark to Databricks-managed Iceberg data via the Iceberg REST Catalog (IRC).
• Use Case: You need to expose your Databricks-managed Iceberg tables to Snowflake for BI reporting, ensuring data consistency and enabling efficient querying across both platforms.

Quick Start

Use the databricks-iceberg skill to create a managed Iceberg table named 'events' in the 'my_catalog.my_schema' catalog with 'event_date' as a partition column.