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databricks-iceberg

Manage Apache Iceberg tables on Databricks with UniForm and Iceberg REST Catalog.

3|1|Updated May 12, 2025
Search Tags:#interoperability#iceberg#databricks#data warehousing#unity catalog#delta lake
One-click install
npx skills add https://github.com/Aradhya0510/databricks-cv-accelerator --skill databricks-iceberg-aradhya0510
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-iceberg
Source: https://github.com/Aradhya0510/databricks-cv-accelerator/tree/main/.github/skills/databricks-iceberg
Command: npx skills add https://github.com/Aradhya0510/databricks-cv-accelerator --skill databricks-iceberg-aradhya0510

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about databricks-iceberg

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert Delta Lake tables to Apache Iceberg on Databricks?▼

Delta-to-Iceberg interoperability on Databricks is achieved using UniForm, which makes Delta tables readable as Iceberg externally. Compatibility Mode extends this support to streaming tables and materialized views for broader cross-engine access.

How does the Iceberg REST Catalog enable external engine access to Databricks?▼

The Iceberg REST Catalog (IRC) connects external tools like Snowflake, PyIceberg, and OSS Spark to Databricks-managed Iceberg data. This facilitates read and write operations across both platforms while ensuring data consistency.

Can I query Databricks-managed Iceberg tables using Snowflake?▼

Yes, you can expose Databricks-managed Iceberg tables to Snowflake for BI reporting. By utilizing the Iceberg REST Catalog, the integration ensures data consistency and enables efficient querying across both platforms.

What is the best way to create native Apache Iceberg tables in Databricks?▼

You can create managed Iceberg tables directly in Databricks by specifying the catalog, schema, table name, and partition columns. For example, create a table named 'events' in 'my_catalog.my_schema' with 'event_date' as a partition column.

Does Unity Catalog support Apache Iceberg table management on Databricks?▼

Yes, Apache Iceberg table management on Databricks is supported within the Unity Catalog ecosystem. It facilitates native table creation, Delta-to-Iceberg interoperability via UniForm, and external engine access through the Iceberg REST Catalog.

When should I use Delta Lake Compatibility Mode over UniForm for Iceberg interoperability?▼

Use UniForm to make Delta tables readable as Iceberg externally for general data sharing. Use Compatibility Mode specifically when you need Iceberg interoperability for streaming tables and materialized views on Databricks.

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