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JingyiWng
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databricks-iceberg

Create and manage Apache Iceberg tables on Databricks with REST Catalog.

Updated Mar 25, 2026
Search Tags:#iceberg#snowflake#databricks#unity-catalog#pyiceberg#external-access
One-click install
npx skills add https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher --skill databricks-iceberg-jingyiwng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-iceberg
Source: https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher/tree/main/.claude/skills/databricks-iceberg
Command: npx skills add https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher --skill databricks-iceberg-jingyiwng

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many organizations need to store data in Apache Iceberg format on Databricks while also allowing external analytics engines, such as Spark, PyIceberg, and Snowflake, to read and write the same tables. Without a unified approach, teams must maintain separate pipelines, handle format conversions, and manage complex permissions.

Core Features & Use Cases

  • Managed Iceberg tables: Native creation, DML, time travel, and predictive optimization directly in Unity Catalog.
  • UniForm & Compatibility Mode: Convert existing Delta tables (including streaming tables and materialized views) to Iceberg‑compatible metadata without migration.
  • Iceberg REST Catalog (IRC): Expose tables to external engines with credential vending and fine‑grained access control.
  • Snowflake interop: Bidirectional integration for reading Databricks Iceberg tables in Snowflake and vice‑versa.
  • Iceberg v3 support: Leverage deletion vectors, VARIANT type, and row lineage for advanced use cases.

Quick Start

Use the databricks-iceberg skill to create a partitioned Iceberg table named events in my_catalog.my_schema.

Frequently Asked Questions about databricks-iceberg

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

FAQPage Schema
How do I create Apache Iceberg tables on Databricks and expose them to external engines?▼

To create Apache Iceberg tables on Databricks and expose them, use the Iceberg REST Catalog (IRC) with Unity Catalog to enable credential vending and fine-grained access control for external engines like PyIceberg and Snowflake.

Can I convert existing Delta tables to Iceberg format without migrating data?▼

Yes, you can convert existing Delta tables to Iceberg format without migrating data by using UniForm and Compatibility Mode, which generates Iceberg-compatible metadata for streaming tables and materialized views directly in Unity Catalog.

How does Snowflake bidirectional integration work with Databricks Iceberg tables?▼

Snowflake bidirectional integration works with Databricks Iceberg tables by allowing Snowflake to read and write Databricks Iceberg tables, and vice-versa, enabling cross-platform analytics through the Iceberg REST Catalog.

Do I need Unity Catalog and a specific Databricks Runtime to use Iceberg v3 features?▼

Yes, you need Unity Catalog and Databricks Runtime 16.1+ to use Apache Iceberg, while Iceberg v3 support requires Databricks Runtime 17.3+ to leverage advanced features like deletion vectors, VARIANT type, and row lineage.

What grants are required for external engines to access Databricks Iceberg tables?▼

External engines require appropriate EXTERNAL USE SCHEMA grants in Unity Catalog to access Databricks Iceberg tables, along with compatible Iceberg client libraries, enabling secure read and write operations across platforms.

What are the limitations of using Delta UniForm for Iceberg compatibility?▼

Delta UniForm generates Iceberg-compatible metadata without migrating data, but it requires Unity Catalog and specific Databricks Runtime versions, and external access depends on compatible Iceberg client libraries and proper EXTERNAL USE SCHEMA grants.

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