Creating Data Lake Table

Sets up managed Iceberg tables through Amazon S3 Tables with automatic compaction and snapshot management. Handles the full workflow from table bucket creation through namespace setup, Glue catalog registration, schema definition, partitioning strategy, and IAM access control. The skill is thorough about checking what exists first to avoid stomping on existing tables, and it enforces lowercase naming because Glue silently rejects mixed case. Works with AWS MCP server tools when available, falls back to CLI otherwise. Note this creates empty tables only. If you have data sitting in S3 already, you'll need the ingesting-into-data-lake skill after this to actually load it.

skills.sh installs: 7K
GitHub stars: 3.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add aws/agent-toolkit-for-aws --skill creating-data-lake-table --agent claude-code

Installs into .claude/skills of the current project.

Files

Create Data Lake Tables with Amazon S3 Tables

Overview

Amazon S3 Tables provides managed Iceberg tables with automatic compaction and snapshot management. Queryable via Athena and Iceberg-compatible engines.

Common Tasks

You MUST use AWS MCP server tools when connected, they provide command validation, sandboxed execution, and audit logging. Fall back to AWS CLI if MCP unavailable.

Decision Guide

Before creating, You MUST check what exists:

You MUST run aws glue get-tables --database-name <NAME> when user mentions a database.

What you find	Action
Fuzzy database name ("our analytics db")	You MUST STOP. Delegate to `finding-data-lake-assets` to resolve.
Non-S3-Tables table with matching name	You MUST STOP. Delegate to `finding-data-lake-assets`. You MUST NOT create until user confirms.
Existing S3 Tables table with matching name	You MUST check schema match. Reuse if compatible, recreate only if user confirms.
No matching tables	Proceed with creation (Steps 1-8).
User explicitly requests new S3 Tables table	Skip checks, proceed with creation.