/connecting-to-data-source

name: connecting-to-data-source
description: >-
  Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server,
  PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints
  from user, discovers existing connections and RDS/Redshift candidates, registers
  credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers
  on: connect to database, set up Glue connection, register data source, connect to
  Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection.
  Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use
  creating-data-lake-table), queries (use querying-data-lake), catalog exploration
  (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).
metadata:
  version: "1"
  argument-hint: "'[source-type|connection-name|hostname]'"

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`

2. Classify the Source

Ask the user which source type they want to connect to, or infer from hints:

| User says... | Source type | Connection type | Reference | |---|---|---|---| | "Oracle", "SQL Server", "Postgres", "MySQL", "RDS \<engine\>" | JDBC database | `JDBC` | [jdbc-setup.md](references/jdbc-setup.md) | | "Redshift", "my cluster", "my data warehouse on AWS" | Redshift | `JDBC` | [jdbc-setup.md](references/jdbc-setup.md) (Redshift section) | | "Snowflake" | Snowflake | `SNOWFLAKE` | [snowflake-setup.md](references/snowflake-setup.md) | | "BigQuery", "Google analytics warehouse" | BigQuery | `BIGQUERY` | [bigquery-setup.md](references/bigquery-setup.md) |

If the user names DynamoDB or a local file, stop and tell them: DynamoDB is read directly by Glue without a connection, and local files belong in the ingesting-into-data-lake skill's local-upload workflow.

3. Gather Connection Hints from the User

You MUST ask for hints the user can provide -- do not guess.

**For all sources:**

• Desired connection name (lowercase, hyphens: `oracle-prod-sales`, `snowflake-analytics`)
• Existing Secrets Manager secret, or create one
• Is source reachable from a Glue VPC (same, peered, VPN, Direct Connect)

**JDBC:** hostname/endpoint, port, database, whether RDS/Aurora/self-managed, IAM DB auth enabled (Aurora/RDS MySQL/Postgres), SSL required.

**Snowflake:** account identifier, warehouse, role, default database, auth (password, key-pair, OAuth).

**BigQuery:** GCP project ID, location, whether service account JSON is provisioned.

4. Discover Existing Connections and Candidate Sources

Check what exists before creating.

**Existing Glue connections:**

aws glue get-connections --filter ConnectionType=<TYPE> --region <REGION>

If a suitable one exists, confirm and skip to Step 7.

**Candidate sources in account** (JDBC/Redshift only):

• RDS: `aws rds describe-db-instances`
• Aurora: `aws rds describe-db-clusters`
• Redshift: `aws redshift describe-clusters`

Present candidates to user; let them pick. See [discovery.md](references/discovery.md).

5. Register Credentials

You MUST encourage AWS Secrets Manager over plaintext passwords. You SHOULD prefer IAM database authentication where supported (Aurora/RDS MySQL and PostgreSQL, Redshift). See [credential-security.md](references/credential-security.md).

• You MUST confirm with user before creating a new Secrets Manager secret
• You MUST NOT write plaintext credentials into chat or logs
• For IAM DB auth, no secret is needed

6. Create the Glue Connection

Follow the source-specific reference for connection properties:

aws glue create-connection --connection-input '<JSON>' --region <REGION>

Private sources require `PhysicalConnectionRequirements` (SubnetId, SecurityGroupIdList, AvailabilityZone). See [network-setup.md](references/network-setup.md).

7. Test the Connection

You MUST test before handing off. Testing is two-phase: a quick API check, then an engine-level verification.

Phase A: Glue TestConnection (network and credential sanity check)

aws glue test-connection --connection-name <NAME> --region <REGION>

This validates that Glue can reach the source and authenticate. It does NOT prove the connection works end-to-end with the query engine the user plans to use.

Phase B: Engine-level verification

After TestConnection passes, verify the connection works with the user's intended engine by running a minimal query through it:

• **Glue ETL (default):** Run a smoke-test Glue job that reads one row via the connection. See [troubleshooting.md](references/troubleshooting.md).
• **Athena:** If the user plans to query via Athena with a federated connector, run a `SELECT 1` through the Athena connection to confirm the Lambda-based connector can reach the source.
• **Glue Crawler:** If the user plans to crawl the source, run a test crawl on a single table.

Phase B catches issues that TestConnection misses: driver compatibility at job runtime, catalog configuration, Spark-level serialization, and engine-specific auth flows (e.g., Snowflake SNOWFLAKE type works in ETL but not via