このページでは、Microsoft SQL Server テーブルからデータを読み取るようにデータ パイプラインを設定する方法について説明します。
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[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[[["\u003cp\u003eThis page provides instructions on setting up a data pipeline to read data from a Microsoft SQL Server table using Cloud Data Fusion.\u003c/p\u003e\n"],["\u003cp\u003eIt details the process of enabling necessary APIs, creating a Cloud Data Fusion instance, and establishing a secure connection to your SQL Server database.\u003c/p\u003e\n"],["\u003cp\u003eThe guide explains how to store your SQL Server password securely using Cloud Data Fusion's secure key feature.\u003c/p\u003e\n"],["\u003cp\u003eInstructions are included for obtaining and deploying the required SQL Server JDBC driver from the Cloud Data Fusion Hub or Pipeline Studio.\u003c/p\u003e\n"],["\u003cp\u003eThe document covers connecting to SQL Server using both Wrangler and the Pipeline Studio within Cloud Data Fusion, including setting connection properties and validating the connection.\u003c/p\u003e\n"]]],[],null,["# Read from a SQL Server table\n\n*** ** * ** ***\n\nThis page describes how to set up your data pipeline to read data from a\nMicrosoft SQL Server table.\n\nBefore you begin\n----------------\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Cloud Data Fusion, BigQuery, Cloud Storage, and Dataproc APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=datafusion.googleapis.com,bigquery.googleapis.com,storage.googleapis.com,dataproc.googleapis.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Cloud Data Fusion, BigQuery, Cloud Storage, and Dataproc APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=datafusion.googleapis.com,bigquery.googleapis.com,storage.googleapis.com,dataproc.googleapis.com)\n\n1.\n\n\n Enable the Cloud Data Fusion, BigQuery, Cloud Storage, and Dataproc APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=datafusion.googleapis.com,bigquery.googleapis.com,storage.googleapis.com,dataproc.googleapis.com)\n2. [Create a Cloud Data Fusion instance](/data-fusion/docs/how-to/create-instance).\n3. Your SQL Server database must accept connections from Cloud Data Fusion. For security reasons, use a [private\n Cloud Data Fusion instance](/data-fusion/docs/how-to/create-private-ip).\n\n### Open your Cloud Data Fusion instance\n\n1. In the Google Cloud console, go to the Cloud Data Fusion **Instances**\n page.\n\n [Go to Instances](https://console.cloud.google.com/data-fusion/locations/-/instances)\n2. In the **Actions** column for the instance, click **View instance** to open\n the instance in Cloud Data Fusion.\n\nStore your SQL Server password as a secure key\n----------------------------------------------\n\nAdd your SQL Server password as a secure key in your Cloud Data Fusion\ninstance.\n\n1. From Cloud Data Fusion, click **System Admin**.\n\n2. Click the **Configuration** tab.\n\n3. Click **Make HTTP Calls**.\n\n \u003cbr /\u003e\n\n4. Select **PUT**.\n\n5. In the path field, enter\n `namespaces/`\u003cvar translate=\"no\"\u003eNAMESPACE_ID\u003c/var\u003e`/securekeys/password\n `.\n\n6. In the **Body** field, enter `{\"data\":\"`\u003cvar translate=\"no\"\u003epassword\u003c/var\u003e`\"}`.\n Replace \u003cvar translate=\"no\"\u003epassword\u003c/var\u003e with your SQL Server password.\n\n7. Click **Send**.\n\nThe **Response** must have status code `200` to continue.\n\nGet the JDBC driver for SQL Server\n----------------------------------\n\nYou can get the driver from the Hub or in the Pipeline Studio in Cloud Data Fusion. \n\n### Hub\n\n1. In the Cloud Data Fusion UI, click **Hub**.\n\n2. In the search bar, enter `SQL Server JDBC Driver` and select the driver.\n\n3. Click **Download**. Follow the download steps shown.\n\n4. Click **Deploy**. Upload the JAR file from the previous step.\n\n5. Click **Finish**.\n\n### Pipeline Studio\n\n1. Go to [Microsoft.com](https://www.microsoft.com/en-us/download/details.aspx?id=11774).\n\n2. Choose your download and click **Download**.\n\n3. In Cloud Data Fusion, click *menu*\n **Menu** and go to the **Pipeline Studio** page.\n\n4. Click add **Add**.\n\n5. For the driver, click **Upload**.\n\n6. Select the JAR file, located in the `jre7` folder.\n\n7. Click **Next**.\n\n8. To configure the driver, enter a **Name** and **Class name**.\n\n9. Click **Finish**.\n\nDeploy the SQL Server Plugin\n----------------------------\n\n1. In Cloud Data Fusion, click **Hub**.\n\n2. In the search bar, enter `SQL Server Plugins`.\n\n3. Click **SQL server plugins**.\n\n4. Click **Deploy**.\n\n5. Click **Finish**.\n\n6. Click **Create a pipeline**.\n\nConnect to SQL Server\n---------------------\n\nYou can connect to SQL Server from Cloud Data Fusion in Wrangler or the Pipeline Studio. \n\n### Wrangler\n\n1. In Cloud Data Fusion, click *menu*\n **Menu** and go to the **Wrangler** page.\n\n2. Click **Add connection**.\n\n An **Add connection** window opens.\n3. Click **SQL Server** to verify that the driver is installed.\n\n \u003cbr /\u003e\n\n4. Enter details in the required connection fields. In the **Password** field, select the\n [secure key you stored previously](#store_your_sql_server_password_as_a_secure_key).\n It ensures that your password is retrieved using [Cloud KMS](/kms/docs).\n\n \u003cbr /\u003e\n\n5. To check that a connection can be established with the database, click\n **Test connection**.\n\n6. Click **Add connection**.\n\nAfter your SQL Server database is connected and you've created a pipeline that\nreads from your SQL Server table, you can apply transformations and\nwrite your output to a sink.\n\n### Pipeline Studio\n\n1. Open your Cloud Data Fusion instance and go to the **Pipeline Studio**\n page.\n\n2. Expand the **Source** menu and click **SQL Server**.\n\n \u003cbr /\u003e\n\n3. On the **SQL Server** node, click **Properties**.\n\n4. In the **Reference name** field, enter a name that\n identifies your SQL Server source.\n\n5. In the **Database** field, enter the name of the database to connect to.\n\n6. In the **Import query** field, enter the query to run. For example,\n `SELECT * FROM table WHERE $CONDITIONS`.\n\n7. Click **Validate**.\n\n8. Click close close.\n\nAfter your SQL Server database is connected and you've created a pipeline that\nreads from your SQL Server table, add any desired transformations and\nwrite your output to a sink.\n\nWhat's next\n-----------\n\n- Learn how to [read data from multiple SQL Server tables](/data-fusion/docs/how-to/reading-from-sqlserver-multi).\n- Learn more about [Cloud Data Fusion](/data-fusion/docs/concepts/overview).\n- Follow one of the [tutorials](/data-fusion/docs/tutorials)."]]
![[Configuration] タブ。](https://cloud.google.com/static/data-fusion/docs/images/how-to/reading-from-sqlserver/config.png?hl=ja)
