Add databricks-iceberg skill#188
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irfanelahi-ds wants to merge 4 commits intodatabricks-solutions:mainfrom
Open
Add databricks-iceberg skill#188irfanelahi-ds wants to merge 4 commits intodatabricks-solutions:mainfrom
irfanelahi-ds wants to merge 4 commits intodatabricks-solutions:mainfrom
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Adds a new skill covering Apache Iceberg on Databricks: - Managed Iceberg tables (DDL, DML, Liquid Clustering, Iceberg v3) - External Iceberg Reads / UniForm and Compatibility Mode for Delta tables - Iceberg REST Catalog (IRC) — auth, credential vending, IP access list guidance - Snowflake interoperability (bidirectional: catalog integration + foreign catalogs) - External engine interop — PyIceberg, OSS Spark, EMR, Flink, Kafka Connect Registers the skill in README.md and install_skills.sh. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Correct factual error: PO is not auto-enabled, it must be explicitly enabled via ALTER ... SET DBPROPERTIES - Add enable examples at catalog, schema, and table levels - Add automatic statistics collection as a PO capability - Add ANALYZE TABLE as the manual statistics collection alternative Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove blockquote repeating PARTITIONED BY/CLUSTER BY behaviour already covered in Critical Rules - Remove 4 Common Issues rows that duplicate Critical Rules: write.metadata.path, Iceberg library in DBR, PARTITIONED BY Liquid Clustering, and CLUSTER BY v2 DV/row-tracking requirement Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Clarify IRC endpoint description in 1-managed-iceberg-tables.md - Improve wording of Future Modes note in 2-uniform-and-compatibility.md - Remove Limitations section from 3-iceberg-rest-catalog.md Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Why this matters
Apache Iceberg is one of the most active areas of customer demand w.r.t interoperating with the wider Iceberg ecosystem. And it has many nuances and user journeys e.g. creating and sharing managed iceberg tables, enabling UniForm on existing delta tables to share with Iceberg clients like Snowflake or Trino without ETL, Reading Foreign Iceberg tables managed by other IRC (e.g., Snowflake) in Databricks or debugging why PyIceberg/OSS Spark can't connect to UC. Without a dedicated skill, Claude has no grounded reference for Databricks-specific Iceberg behaviour and falls back on generic Iceberg docs that don't reflect how UC actually implements things (e.g.
PARTITIONED BYmapping to Liquid Clustering, the IRC endpoint path,EXTERNAL USE SCHEMArequirements, vended credential flow etc).This PR closes that gap.
What's included
1-managed-iceberg-tables.mdPARTITIONED BYvsCLUSTER BY), Predictive Optimization, Iceberg v3, limitations2-uniform-and-compatibility.md3-iceberg-rest-catalog.md4-snowflake-interop.md5-external-engine-interop.mdSome Examples of Specific problems this solves for customers
PARTITIONED BYvsCLUSTER BYconfusion: Clarifies that both produce Liquid Clustering and identical Iceberg metadata for external engines, and exactly when each requires TBLPROPERTIES changes