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databricks-iceberg

Skill

Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External

0Databases

Install

git clone https://github.com/databricks/databricks-agent-skills.git ~/.claude/skills/databricks-iceberg

What is databricks-iceberg?

Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~140 tokens of context used while enabled, before you invoke anything

Documentation

README · ~5 min read

Apache Iceberg on Databricks

Databricks provides multiple ways to work with Apache Iceberg: native managed Iceberg tables, UniForm for Delta-to-Iceberg interoperability, and the Iceberg REST Catalog (IRC) for external engine access.

Critical Rules (always follow)

  • MUST use Unity Catalog — all Iceberg features require UC-enabled workspaces
  • MUST NOT install an Iceberg library into Databricks Runtime (DBR includes built-in Iceberg support; adding a library causes version conflicts)
  • MUST NOT set write.metadata.path or write.metadata.previous-versions-max — Databricks manages metadata locations automatically; overriding causes corruption
  • MUST determine which Iceberg pattern fits the use case before writing code — see the When to Use section below
  • MUST know that both PARTITIONED BY and CLUSTER BY produce the same Iceberg metadata for external engines — UC maintains an Iceberg partition spec with partition fields corresponding to the clustering keys, so external engines reading via IRC see a partitioned Iceberg table (not Hive-style, but proper Iceberg partition fields) and can prune on those fields; internally UC uses those fields as liquid clustering keys; the only differences between the two syntaxes are: (1) PARTITIONED BY is standard Iceberg DDL (any engine can create the table), while CLUSTER BY is DBR-only DDL; (2) PARTITIONED BY auto-handles DV/row-tracking properties, while CLUSTER BY requires manual TBLPROPERTIES on v2
  • MUST NOT use expression-based partition transforms (bucket(), years(), months(), days(), hours()) with PARTITIONED BY on managed Iceberg tables — only plain column references are supported; expression transforms cause errors

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