connecting-to-data-source
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).
下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o connecting-to-data-source.zip https://jpskill.com/download/23343.zip && unzip -o connecting-to-data-source.zip && rm connecting-to-data-source.zip
$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/23343.zip -OutFile "$d\connecting-to-data-source.zip"; Expand-Archive "$d\connecting-to-data-source.zip" -DestinationPath $d -Force; ri "$d\connecting-to-data-source.zip"
完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。
💾 手動でダウンロードしたい(コマンドが難しい人向け)
- 1. 下の青いボタンを押して
connecting-to-data-source.zipをダウンロード - 2. ZIPファイルをダブルクリックで解凍 →
connecting-to-data-sourceフォルダができる - 3. そのフォルダを
C:\Users\あなたの名前\.claude\skills\(Win)または~/.claude/skills/(Mac)へ移動 - 4. Claude Code を再起動
⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。
🎯 このSkillでできること
下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。
📦 インストール方法 (3ステップ)
- 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
- 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
- 3. 展開してできたフォルダを、ホームフォルダの
.claude/skills/に置く- · macOS / Linux:
~/.claude/skills/ - · Windows:
%USERPROFILE%\.claude\skills\
- · macOS / Linux:
Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。
詳しい使い方ガイドを見る →- 最終更新
- 2026-05-18
- 取得日時
- 2026-05-18
- 同梱ファイル
- 8
📖 Claude が読む原文 SKILL.md(中身を展開)
この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。
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-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 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 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
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 modes - bigquery-setup.md -- Glue
BIGQUERYtype, GCP service accounts - discovery.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
同梱ファイル
※ ZIPに含まれるファイル一覧。`SKILL.md` 本体に加え、参考資料・サンプル・スクリプトが入っている場合があります。
- 📄 SKILL.md (8,918 bytes)
- 📎 references/bigquery-setup.md (2,645 bytes)
- 📎 references/credential-security.md (4,400 bytes)
- 📎 references/discovery.md (3,668 bytes)
- 📎 references/jdbc-setup.md (3,569 bytes)
- 📎 references/network-setup.md (4,010 bytes)
- 📎 references/snowflake-setup.md (2,687 bytes)
- 📎 references/troubleshooting.md (7,810 bytes)