Ingesting Into Data Lake

Moves data from various sources into AWS data lakes, targeting either S3 Tables or standard Iceberg depending on what you've already adopted. Handles the full range: local files, S3, JDBC databases (Oracle, Postgres, MySQL, SQL Server), Redshift, Snowflake, BigQuery, DynamoDB, and Glue catalog migrations. The philosophy is smart: it checks your existing catalog setup and recommends S3 Tables for new work but doesn't force you off standard Iceberg if that's what you're using. Includes validation steps, scheduling options, and clear gotchas like the Spark catalog config quirks in Glue 5.x that will bite you if you put settings in the wrong place. Explicitly punts SaaS sources like Salesforce and Kafka to keep scope tight.

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 ingesting-into-data-lake --agent claude-code

Installs into .claude/skills of the current project.

Files

Ingest into Data Lake

Move data from a source into a queryable table in the data lake. This skill assumes the source connection (if one is needed) already exists. For Glue connection setup or troubleshooting, delegate to connecting-to-data-source.

Philosophy

Default to S3 Tables unless the environment says otherwise. S3 Tables is the recommended target for new data lake work. If the user's catalog inventory shows they haven't adopted S3 Tables, recommend standard Iceberg on their existing general-purpose bucket instead of forcing them to change posture.

Common Tasks

You MUST execute commands using AWS MCP server tools when connected -- they provide validation, sandboxed execution, and audit logging. Fall back to AWS CLI only if MCP is unavailable. You MUST explain each step before executing.