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Connect to Data Source

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Easily create and troubleshoot AWS Glue database connections.

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What Connect to Data Source does

The Connect to Data Source skill enables users to establish and manage AWS Glue connections to various data sources, including JDBC databases such as Oracle, SQL Server, PostgreSQL, MySQL, and Amazon RDS, as well as data warehouses like Redshift, Snowflake, and BigQuery. This skill is particularly useful for data engineers and developers who need to connect external data sources to AWS Glue for data processing and analysis. By streamlining the connection setup process, it allows users to focus on data ingestion and transformation tasks without getting bogged down in connection configuration details.

The workflow begins with verifying the necessary dependencies, ensuring that AWS MCP tools or AWS CLI are available, and confirming the target AWS region and user credentials. Users are guided through classifying the source type and gathering essential connection hints, such as desired connection names and credential management preferences. The skill also checks for existing connections and candidate sources, helping to avoid duplicate configurations and ensuring efficient resource management.

Once the connection details are gathered, the skill facilitates the registration of credentials, favoring AWS Secrets Manager for enhanced security. After creating the Glue connection, it performs thorough testing to validate both network accessibility and engine-level compatibility. This two-phase testing process helps catch potential issues that may arise during actual data processing, ensuring that users can rely on their connections for downstream tasks.

Overall, this skill is designed for developers and data engineers who require a reliable method for connecting AWS Glue to various data sources, while also providing troubleshooting capabilities for connection issues. It simplifies the process of setting up and managing connections, allowing users to focus on their data workflows rather than connection complexities.

When to use it

Use this skill when you need to establish a connection between AWS Glue and external databases or data warehouses for data processing.

When not to use it

This skill is not suitable for data movement, table creation, querying, or catalog exploration tasks; those require different skills.

What you can build with it

Connecting to a Snowflake Data Warehouse

Use this skill to set up a connection to your Snowflake data warehouse, ensuring all necessary parameters are configured correctly.

Establishing JDBC Connections

Quickly create JDBC connections to databases like MySQL or PostgreSQL, streamlining your data pipeline setup.

Troubleshooting Connection Issues

Utilize the troubleshooting features of this skill to diagnose and resolve connection problems efficiently.

How to install Connect to Data Source

View source

1. Install with the skills CLI

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

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by aws

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 typeConnection typeReference
"Oracle", "SQL Server", "Postgres", "MySQL", "RDS <engine>"JDBC databaseJDBCjdbc-setup.md
"Redshift", "my cluster", "my data warehouse on AWS"RedshiftJDBCjdbc-setup.md (Redshift section)
"Snowflake"SnowflakeSNOWFLAKEsnowflake-setup.md
"BigQuery", "Google analytics warehouse"BigQueryBIGQUERYbigquery-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.

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.

  • 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.

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.
  • 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 JDBC crawlers).

On success in both phases, tell user the connection name is ready for ingesting-into-data-lake. On failure in either phase, Step 8.

8. Troubleshoot (only if test failed)

Diagnose in order: network, credentials, driver. See troubleshooting.md.

Constraints:

  • You MUST check VPC routing, security groups, and S3 VPC endpoint before blaming credentials
  • You MUST verify Glue role can read the Secrets Manager secret
  • You MUST NOT rotate credentials without user confirmation

Argument Routing

  • No args: Walk through Steps 1-7 interactively
  • Source type keyword (e.g., snowflake, oracle): Skip to Step 2 with the type prefilled
  • Existing connection name: Skip to Step 7 (test) then Step 8 if failing
  • Hostname or RDS endpoint: Skip to Step 4 with the candidate prefilled

Gotchas

  • Glue's SNOWFLAKE connection type is distinct from JDBC configured for Snowflake. You MUST use SNOWFLAKE for Spark ETL jobs; do not use JDBC.
  • Connection names are immutable. Choose carefully.
  • PhysicalConnectionRequirements.AvailabilityZone MUST match the subnet's AZ or the connection fails at job runtime, not creation time.
  • IAM database authentication tokens expire in 15 minutes. The Glue job generates a fresh token on each connection; do not cache.
  • An S3 VPC gateway endpoint MUST exist in the VPC used by private-source connections. Without it, Glue jobs cannot read their scripts or write results to S3.

Troubleshooting

ErrorLikely causeFix
Connect timed outVPC routing, SG rule, or NAT gateway missingSee troubleshooting.md
Access denied for user / ORA-01017Credentials wrong, Secrets Manager access missing, or IAM DB auth misconfiguredSee troubleshooting.md
No suitable driver foundCustom driver JAR not set or wrong class nameSee troubleshooting.md
SSL handshake failedJDBC_ENFORCE_SSL mismatch between Glue and sourceSee troubleshooting.md
UnableToFindVpcEndpointS3 VPC endpoint missingCreate S3 gateway endpoint in the connection's VPC

References

Frequently asked questions about Connect to Data Source

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