Connecting To Data Source

Sets up AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery so downstream jobs can pull data from them. Walks you through credential registration in Secrets Manager, VPC configuration for private sources, and a two-phase test that checks both network reachability and actual query engine compatibility. The philosophy is sound: a connection is a named pipe, not a pipeline, so this strictly handles registration and stops before any data movement. One gotcha worth knowing upfront is that Glue's SNOWFLAKE connection type is completely different from JDBC-to-Snowflake, and you must pick the right one for Spark ETL or you'll troubleshoot phantom driver issues.

skills.sh installs: 7.2K
GitHub stars: 3.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add aws/agent-toolkit-for-aws --skill connecting-to-data-source --agent claude-code

Installs into .claude/skills of the current project.

Files

Connect to Data Source

Register an external data source with AWS Glue so downstream skills (ingesting-into-data-lake) can move data from it. A Glue connection stores the network config, driver, and credential reference for one source. Create once per source, reuse across jobs.

Philosophy

A connection is a named pipe, not a pipeline. This skill produces a tested, reusable Glue connection. It does not move data.

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.

Workflow

1. Verify Dependencies and Context

You MUST check whether AWS MCP tools or AWS CLI are available and inform the user if missing
You MUST confirm target AWS region and verify credentials with aws sts get-caller-identity