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databricks-iceberg

Manage and access Apache Iceberg tables within Databricks.

Updated Jul 4, 2026
Search Tags:#Data Management#Iceberg#Databricks#Unified Catalog#Interoperability
One-click install
npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-iceberg-mkgs-databricks-demos
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-iceberg
Source: https://github.com/mkgs-databricks-demos/aiSkillUpdater/tree/main/databricks-iceberg
Command: npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-iceberg-mkgs-databricks-demos

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unity-catalog, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for managing and accessing Apache Iceberg tables within Databricks, ensuring seamless interoperability with various data sources and external systems.

Core Features & Use Cases

  • Managed Iceberg Tables: Create and manage native Iceberg tables with full read/write support.
  • External Iceberg Reads: Enable Delta tables to be readable as Iceberg externally, leveraging features like UniForm and Compatibility Mode.
  • Interoperability: Access and read Databricks tables via the Iceberg REST Catalog (IRC) for external engines like Snowflake and PyIceberg.
  • Use Case: When you need to analyze Iceberg tables within Databricks or access them from external systems like Snowflake or PySpark.

Quick Start

Use the databricks-iceberg skill to create a managed Iceberg table for storing event data with the following DDL command:

CREATE TABLE my_catalog.my_schema.events
USING ICEBERG
AS SELECT * FROM raw_events;

Frequently Asked Questions about databricks-iceberg

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

FAQPage Schema
How do I create and manage Apache Iceberg tables in Databricks?▼

Databricks supports creating managed Apache Iceberg tables with full read and write operations using standard SQL DDL commands. You must have Unity Catalog configured and a Databricks runtime with Iceberg support enabled.

Can external engines like Snowflake read Databricks Delta tables as Iceberg?▼

Yes, external engines like Snowflake can read Databricks Delta tables as Iceberg via the Iceberg REST Catalog (IRC). UniForm and Compatibility Mode enable this external access for interoperability across different query engines.

Do I need Unity Catalog to use Iceberg tables on Databricks?▼

Yes, Unity Catalog is a required dependency for managing and accessing Apache Iceberg tables within Databricks. It provides the unified platform for data management and interoperability with external engines.

What is the Iceberg REST Catalog and how does it enable interoperability?▼

The Iceberg REST Catalog (IRC) enables interoperability by providing external engines like Snowflake and PyIceberg with standardized access to read Databricks tables. This allows seamless querying across different data platforms.

What is the best way to convert Delta table data to Iceberg format?▼

To convert Delta table data to Iceberg format, use UniForm or Compatibility Mode features. These enable Delta tables to be readable as Iceberg externally, allowing interoperability without duplicating data.

Are there limitations when writing to Iceberg tables from external systems?▼

External systems can read Databricks tables via the Iceberg REST Catalog, but full read and write operations are supported for managed Iceberg tables created directly within Databricks using the appropriate runtime.

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