Connecting To Data Source
AWSOfficialPublished by AWS
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for registering external data sources with AWS Glue. It handles the creation of Glue connections, which act as reusable network and credential configurations for ETL jobs. The process includes discovering existing resources, setting up secure credential storage via AWS Secrets Manager or IAM database authentication, configuring VPC network requirements, and performing end-to-end connectivity testing to ensure the connection is ready for production use.
What it does
- Discovers existing RDS, Redshift, and Glue connection configurations.
- Registers database credentials securely using AWS Secrets Manager or IAM DB authentication.
- Configures VPC, subnet, and security group requirements for private data sources.
- Creates Glue connection objects for JDBC databases, Redshift, Snowflake, and BigQuery.
- Performs multi-phase connectivity testing including API checks and engine-level smoke tests.
- Provides diagnostic steps for common network, credential, and driver-related connection failures.
When to use it
- When you need to establish a new connection between AWS Glue and an external database or warehouse.
- When you need to troubleshoot an existing Glue connection that is failing during ETL jobs.
- When you are setting up a new data source for use with Glue ETL or Athena federated queries.
- When you need to migrate from plaintext credentials to secure AWS Secrets Manager or IAM-based auth.
Advantages
- Ensures secure credential handling by prioritizing Secrets Manager and IAM over plaintext.
- Validates network and authentication settings before they are used in production jobs.
- Reduces configuration errors by providing source-specific setup patterns.
- Provides a clear, repeatable process for managing data source connectivity across environments.
Usage examples
- “Set up a new Glue connection for my PostgreSQL RDS instance.”
- The agent will guide you through identifying the RDS instance, configuring the VPC settings, setting up IAM DB authentication or a secret, and running a test connection.
- “I need to connect Glue to Snowflake.”
- The agent will prompt for your Snowflake account details, warehouse, and role, then create a native SNOWFLAKE-type Glue connection and verify it.
- “Troubleshoot my Glue connection 'oracle-prod-sales' which is timing out.”
- The agent will check your VPC routing, security group rules, and S3 VPC endpoints to diagnose the connectivity issue.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/connecting-to-data-source/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip connecting-to-data-source.zip -d .agents/skills
Documentation 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 type | Connection type | Reference |
|---|---|---|---|
| "Oracle", "SQL Server", "Postgres", "MySQL", "RDS <engine>" | JDBC database | JDBC | jdbc-setup.md |
| "Redshift", "my cluster", "my data warehouse on AWS" | Redshift | JDBC | jdbc-setup.md (Redshift section) |
| "Snowflake" | Snowflake | SNOWFLAKE | snowflake-setup.md |
| "BigQuery", "Google analytics warehouse" | BigQuery | BIGQUERY | 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-instancesAurora:
aws rds describe-db-clustersRedshift:
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 1through 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 prefilledExisting 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
SNOWFLAKEconnection type is distinct fromJDBCconfigured for Snowflake. You MUST useSNOWFLAKEfor Spark ETL jobs; do not use JDBC.Connection names are immutable. Choose carefully.
PhysicalConnectionRequirements.AvailabilityZoneMUST 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
| Error | Likely cause | Fix |
|---|---|---|
Connect timed out | VPC routing, SG rule, or NAT gateway missing | See troubleshooting.md |
Access denied for user / ORA-01017 | Credentials wrong, Secrets Manager access missing, or IAM DB auth misconfigured | See troubleshooting.md |
No suitable driver found | Custom driver JAR not set or wrong class name | See troubleshooting.md |
SSL handshake failed | JDBC_ENFORCE_SSL mismatch between Glue and source | See troubleshooting.md |
UnableToFindVpcEndpoint | S3 VPC endpoint missing | Create S3 gateway endpoint in the connection's VPC |
References
jdbc-setup.md -- Oracle, SQL Server, PostgreSQL, MySQL, RDS, Redshift
snowflake-setup.md -- Glue
SNOWFLAKEtype, auth modesbigquery-setup.md -- Glue
BIGQUERYtype, GCP service accountsdiscovery.md -- Finding existing connections and candidate sources
credential-security.md -- Secrets Manager and IAM DB auth
network-setup.md -- VPC, subnets, security groups, endpoints
troubleshooting.md -- Connection errors and diagnostic flow
Files
- ATTRIBUTION.mdAdded by AutoGineer930 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md8.7 KiB
- references/bigquery-setup.md2.6 KiB
- references/credential-security.md4.3 KiB
- references/discovery.md3.6 KiB
- references/jdbc-setup.md3.5 KiB
- references/network-setup.md3.9 KiB
- references/snowflake-setup.md2.6 KiB
- references/troubleshooting.md7.6 KiB
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs a command-line tool
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- AWS
- Publisher
- AWS (Official)
- Source
- aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/connecting-to-data-source · commit 0a218f8
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- aws/agent-toolkit-for-aws
AutoGineer is not affiliated with or endorsed by AWS or AWS.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download connecting-to-data-source.zip