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Connecting To Data Source

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • No issues found
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From the author

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).
— the description by AWS

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

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/connecting-to-data-source/download

  2. Unzip 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 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

Files

references/credential-security.md

Credential Security

Order of preference for authenticating Glue connections to data sources:

  1. IAM database authentication (where supported)

  2. AWS Secrets Manager (SECRET_ID)

  3. Plaintext USERNAME/PASSWORD in connection properties (not recommended)

Contents

  • IAM Database Authentication

  • AWS Secrets Manager

  • Plaintext Credentials

  • Rotation

IAM Database Authentication

Supported sources:

  • Aurora MySQL, Aurora PostgreSQL

  • RDS MySQL, RDS PostgreSQL

  • Amazon Redshift (via GetClusterCredentials / GetCredentials)

Benefits:

  • No long-lived database passwords

  • No secret to rotate

  • Database access controlled by IAM policies

  • Audit trail via CloudTrail

RDS / Aurora Setup

  1. Enable IAM DB auth on the cluster or instance:

    aws rds modify-db-instance \
  --db-instance-identifier <ID> \
  --enable-iam-database-authentication \
  --apply-immediately

  2. Create a DB user that authenticates via IAM (MySQL):

    CREATE USER 'etl_user'@'%' IDENTIFIED WITH AWSAuthenticationPlugin AS 'RDS';
GRANT SELECT ON app_db.* TO 'etl_user'@'%';

    PostgreSQL:

    CREATE USER etl_user;
GRANT rds_iam TO etl_user;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO etl_user;

  3. Grant the Glue job role the rds-db:connect action:

    {
  "Effect": "Allow",
  "Action": "rds-db:connect",
  "Resource": "arn:aws:rds-db:<region>:<account>:dbuser:<resource-id>/etl_user"
}

  4. In the Glue connection, omit SECRET_ID, USERNAME, and PASSWORD. Glue generates an auth token on each connection.

Redshift Setup

Grant the Glue role redshift:GetClusterCredentials (provisioned) or redshift-serverless:GetCredentials (serverless), scoped to the cluster/workgroup and DB user.

Configure the connection with the Redshift endpoint and a DB user. No password.

AWS Secrets Manager

When IAM DB auth is not available (Oracle, SQL Server, Snowflake, BigQuery, self-managed), use Secrets Manager.

Create Secret

JDBC sources:

aws secretsmanager create-secret \
  --name glue/<connection-name>/credentials \
  --secret-string '{"username":"etl_user","password":"<password>"}' \
  --region <region>

Snowflake (key names are Glue-specific):

aws secretsmanager create-secret \
  --name glue/snowflake-analytics/credentials \
  --secret-string '{"snowflakeUser":"ETL_USER","snowflakePassword":"<password>"}' \
  --region <region>

BigQuery (base64 of service account JSON, stored as the secret string directly):

base64 -i <sa>.json | tr -d '\n' | \
aws secretsmanager create-secret \
  --name glue/bigquery/<project-id>/credentials \
  --secret-string file:///dev/stdin \
  --region <region>
Grant Glue Role Access
{
  "Effect": "Allow",
  "Action": "secretsmanager:GetSecretValue",
  "Resource": "arn:aws:secretsmanager:<region>:<account>:secret:glue/<connection-name>/credentials-*"
}

The -* suffix matches the random 6-character suffix Secrets Manager appends.

Reference in Connection
"ConnectionProperties": {
  "JDBC_CONNECTION_URL": "...",
  "SECRET_ID": "glue/<connection-name>/credentials"
}

Omit USERNAME and PASSWORD. Glue reads them from the secret at job runtime.

Plaintext Credentials

Not recommended. Use only for:

  • Disposable developer sandboxes

  • Sources where Secrets Manager integration is not supported by the Glue connector

If you must, use USERNAME and PASSWORD in ConnectionProperties. The password is encrypted at rest in the Data Catalog but visible in get-connection responses to any principal with glue:GetConnection.

Rotation

Secrets Manager rotation:

  • Enable automatic rotation on the secret (7, 30, 60, or 90 days)

  • Rotation Lambda updates the password in the source database and writes the new value to the secret

  • Glue picks up the new value on the next job run; no connection update needed

  • For Aurora/RDS, use the AWS-provided rotation template

IAM DB auth: no rotation -- tokens are minted per-connection and expire in 15 minutes.

Service account keys (BigQuery) / key-pairs (Snowflake): rotate by generating a new key at the source, updating the Secrets Manager value, and letting the old key expire or be deleted in the source.

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
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aws/agent-toolkit-for-aws

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