creating-data-lake-table
An agent skill by aws, from aws/agent-toolkit-for-aws. Tags: aws, data, database, glue, iceberg.
What it does
Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management. Sets up table bucket, namespace, table, schema, Glue catalog registration, partitioning, IAM access control. Triggers on: create table, data lake table, analytics table, structured data storage, S3 Tables, Iceberg, Athena table, partitioning strategy, access permissions. Do NOT use for: importing files (use ingesting-into-data-lake), vector storage (use storing-and-querying-vectors), querying existing tables (use querying-data-lake), or locating existing table (use finding-data-lake-assets).
Install
With the skills CLI, which installs into Claude Code, Codex, Cursor and other agents:
npx skills add aws/agent-toolkit-for-aws --skill creating-data-lake-tableOr copy the skill folder into Claude Code's skills directory by hand (~/.claude/skills for every project, or .claude/skills inside one):
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws
cp -r agent-toolkit-for-aws/plugins/aws-data-analytics/skills/creating-data-lake-table ~/.claude/skills/creating-data-lake-tableSafety box score
Not rated yet. A safety box score grades what a skill and its scripts can reach on the machine of whoever installs it, across eight categories from shell execution to secrets access. Anyone can request one from this page; it is saved for everyone. How the score works.
Source
- Repository
- aws/agent-toolkit-for-aws (all skills from this repository)
- Path
- plugins/aws-data-analytics/skills/creating-data-lake-table/SKILL.md
- Branch
- main
- Collection
- aws-data-analytics
- Updated
- 2026-09-19
Related skills
- connecting-to-data-source — Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery.
- exploring-data-catalog — Full inventory and audit of AWS Glue Data Catalog assets across S3 Tables, Redshift-federated, and remote Iceberg catalogs.
- finding-data-lake-assets — Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift.
- ingesting-into-data-lake — Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift.
- storing-and-querying-vectors — Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors).
- amazon-neptune — Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph.