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ThomazRossito
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databricks-iceberg

Creates Iceberg tables in Databricks with Unity Catalog for external engine access via credential-vended REST endpoint.

31|18|Updated Apr 4, 2026
Search Tags:#iceberg#snowflake#databricks#unity-catalog#compatibility-mode#external-access
One-click install
npx skills add https://github.com/ThomazRossito/data-agents --skill databricks-iceberg-thomazrossito
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-iceberg
Source: https://github.com/ThomazRossito/data-agents/tree/main/skills/databricks/databricks-iceberg
Command: npx skills add https://github.com/ThomazRossito/data-agents --skill databricks-iceberg-thomazrossito

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Organizations using Databricks need a unified way to store data as Apache Iceberg tables while still allowing external analytics engines (Spark, PyIceberg, Snowflake, etc.) to read and write that data directly, without maintaining separate pipelines or formats.

Core Features & Use Cases

  • Managed Iceberg tables: Full read/write support in Databricks using Unity Catalog.
  • UniForm (Delta‑to‑Iceberg): Makes existing Delta tables appear as Iceberg to external engines.
  • Compatibility Mode: Extends UniForm to streaming tables and materialized views.
  • Iceberg REST Catalog (IRC): Provides credential‑vended, secure REST endpoint for external engine access.
  • Snowflake Interoperability: Bidirectional integration for reading and writing Iceberg tables between Databricks and Snowflake.

Quick Start

Create a managed Iceberg table and enable UniForm on an existing Delta table using Databricks SQL.

Frequently Asked Questions about databricks-iceberg

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

FAQPage Schema
How do I create native Iceberg tables on Databricks for external engine access?▼

To create native Iceberg tables on Databricks, you use Unity Catalog to enable full read/write support for external engines. This requires Databricks Runtime 16+ and proper EXTERNAL USE SCHEMA grants to credential vending for secure access.

Does Databricks support bidirectional Iceberg read and write with Snowflake?▼

Yes, Databricks supports bidirectional Snowflake interoperability for reading and writing Iceberg tables. This integration allows both Databricks and Snowflake to directly access and modify the same underlying data.

How does UniForm convert existing Delta tables to Iceberg?▼

UniForm makes existing Delta tables appear as Iceberg to external engines like Spark and PyIceberg. Compatibility Mode further extends this UniForm functionality to support streaming tables and materialized views.

What do I need to access Databricks Iceberg tables from external analytics engines?▼

Accessing Databricks Iceberg tables from external analytics engines requires the Iceberg REST Catalog (IRC). This provides a credential-vended, secure REST endpoint for external Spark, PyIceberg, and Snowflake engines.

Do I need Unity Catalog to manage Iceberg tables in Databricks?▼

Yes, Unity Catalog is a strict requirement for managing native Iceberg tables in Databricks. You also need Databricks Runtime 16+ and must configure proper EXTERNAL USE SCHEMA grants to enable credential vending.

Why use managed Iceberg tables instead of maintaining separate data pipelines?▼

Managed Iceberg tables provide a unified format for data lake workloads, allowing Databricks and external engines to read and write directly. This eliminates the need to maintain separate pipelines or formats for cross-platform analytics.

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