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ThomazRossito
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databricks-iceberg

Create and manage Apache Iceberg tables on Databricks with external engine interoperability.

4|1|Updated May 22, 2026
Search Tags:#Snowflake#PyIceberg#databricks#Unity Catalog#uniform#Apache Iceberg#Iceberg REST Catalog
One-click install
npx skills add https://github.com/ThomazRossito/ai-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/ai-data-agents/tree/main/plugins/ai-data-agents/skills/databricks-iceberg
Command: npx skills add https://github.com/ThomazRossito/ai-data-agents --skill databricks-iceberg-thomazrossito

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Working with Apache Iceberg on Databricks involves choosing between managed Iceberg tables, UniForm, and Compatibility Mode, each with different DDL syntax, table properties, and external engine access patterns. This Skill provides the correct SQL, configuration, and troubleshooting guidance so tables are created properly and remain readable by external engines like Snowflake, PyIceberg, and OSS Spark. ## Core Features & Use Cases - Managed Iceberg Tables: Create native Iceberg tables with USING ICEBERG, including Liquid Clustering via PARTITIONED BY or CLUSTER BY, DML, time travel, and Iceberg v3 features. - UniForm & Compatibility Mode: Enable External Iceberg Reads on existing Delta tables, streaming tables, and materialized views without migration. - External Engine Access: Configure the Iceberg REST Catalog (IRC) endpoint, credential vending, EXTERNAL USE SCHEMA grants, and per-engine connections for Snowflake, PyIceberg, OSS Spark, EMR, and Flink. - Use Case: You need Snowflake analysts to query a Databricks Delta table. Use this Skill to enable UniForm on the table, grant external access, and set up a Snowflake catalog integration against the IRC endpoint. ## Quick Start Ask the AI to create a managed Iceberg table in Unity Catalog partitioned by date, or to enable UniForm on an existing Delta table so Snowflake can read it.

Frequently Asked Questions about databricks-iceberg

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

FAQPage Schema
How do I create an Iceberg table in Databricks?▼

Use CREATE TABLE with USING ICEBERG in a Unity Catalog schema on DBR 16.4 LTS or later. Add PARTITIONED BY for cross-platform clustering, which auto-handles deletion vector and row tracking properties, or CLUSTER BY with manual TBLPROPERTIES on Iceberg v2.

What is the difference between managed Iceberg and UniForm in Databricks?▼

Managed Iceberg tables are native Iceberg with full read/write from Databricks and external engines via IRC. UniForm keeps the table as Delta internally but generates Iceberg metadata so external engines can read it as Iceberg, with writes remaining Delta-only.

Can Snowflake read Databricks Iceberg tables?▼

Yes, Snowflake reads Databricks tables through a catalog integration pointing to the Databricks Iceberg REST Catalog endpoint. On AWS, set ACCESS_DELEGATION_MODE to VENDED_CREDENTIALS and grant EXTERNAL USE SCHEMA on the Databricks side.

Does UniForm work on streaming tables and materialized views?▼

No, regular UniForm does not support streaming tables or materialized views. Use Compatibility Mode, which creates a read-only Iceberg-compatible copy at a specified external location and syncs changes incrementally.

Why does my external engine get 403 errors connecting to Databricks IRC?▼

A 403 with valid credentials usually means the workspace IP access list blocks the client's egress IPs, or the principal lacks the EXTERNAL USE SCHEMA grant. Check the IP access list in workspace security settings first, then verify grants.

What are the limitations of managed Iceberg tables on Databricks?▼

Managed Iceberg tables do not support Change Data Feed, Vector Search, shallow clones, or Structured Streaming sinks. They use Parquet only, default to zstd compression, and expression-based partition transforms like bucket() are not supported.

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