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This page describes how to read multiple tables from a Microsoft SQL Server
database, using the Multi Tablesource.
Use the Multi Table source when you want your pipeline to read from
multiple tables. If you want your pipeline to read from a single table, see
Reading from a SQL Server table.
The Multi Table source outputs data with multiple schemas and includes a
table name field that indicates the table from which the data came. When
using the Multi Table source, use one of the multi table sinks,
BigQuery Multi Table or GCS Multi File.
Before you begin
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
When using Cloud Data Fusion, you use both the Google Cloud console
and the separate Cloud Data Fusion UI. In the Google Cloud console, you
can create a Google Cloud project, and create and delete
Cloud Data Fusion instances. In the Cloud Data Fusion UI, you can use
the various pages, such as Studio or Wrangler, to use
Cloud Data Fusion features.
In the Google Cloud console, go to the Cloud Data Fusion page.
To open the instance in the Cloud Data Fusion Studio,
click Instances, and then click View instance.
Add your SQL Server password as a secure key to encrypt on your
Cloud Data Fusion instance. Later in this guide, you will ensure that
your password is retrieved using Cloud KMS.
In the top-right corner of any Cloud Data Fusion page, click System
Admin.
Click the Configuration tab.
Click Make HTTP Calls.
In the dropdown menu, choose PUT.
In the path field, enter namespaces/NAMESPACE_ID/securekeys/PASSWORD.
In the Body field, enter {"data":"SQL_SERVER_PASSWORD"}.
Click Send.
Ensure that the Response you get is status code 200.
Get the JDBC driver for SQL Server
Using the Hub
In the Cloud Data Fusion UI, click Hub.
In the search bar, enter Microsoft SQL Server JDBC Driver.
Click Microsoft SQL Server JDBC Driver.
Click Download. Follow the download steps shown.
Click Deploy. Upload the JAR file from the previous step.
In the Cloud Data Fusion UI, click menuMenu and navigate to the Studio page.
Click addAdd.
Under Driver, click Upload.
Upload the JAR file downloaded in step 2.
Click Next.
Configure the driver by entering a Name.
In the Class name field, enter com.microsoft.sqlserver.jdbc.SQLServerDriver.
Click Finish.
Deploy the Multiple Table Plugins
In the Cloud Data Fusion web UI, click Hub.
In the search bar, enter Multiple table plugins.
Click Multiple Table Plugins.
Click Deploy.
Click Finish.
Click Create a Pipeline.
Connect to SQL Server
In the Cloud Data Fusion UI, click menuMenu and navigate to the Studio page.
In Studio, expand the Source menu.
Click Multiple Database Tables.
Hold the pointer over the Multiple Database Tables node and click
Properties.
In the Reference name field, specify a reference name that will be used to
identify your SQL Server source.
In the JDBC Connection String field, enter the JDBC connection string. For
example, jdbc:sqlserver://mydbhost:1433. For more information, see
Building the connection URL.
Enter the JDBC Plugin Name, Database User Name, and
Database User Password.
Click Validate.
Click closeClose.
Connect to BigQuery or Cloud Storage
In the Cloud Data Fusion UI, click menuMenu and navigate to the Studio page.
Expand Sink.
Click BigQuery Multi Table or GCS Multi File.
Connect the Multiple Database Tables node with BigQuery Multi Table
or GCS Multi File.
Hold the pointer over the BigQuery Multi Table
or GCS Multi File node, click Properties, and configure the sink.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eThis guide outlines the process of reading data from multiple Microsoft SQL Server tables using the Cloud Data Fusion Multi Table source.\u003c/p\u003e\n"],["\u003cp\u003eThe Multi Table source is used when a pipeline needs to read from multiple tables, in contrast to using a single table source, and it outputs data with multiple schemas while providing a table name field.\u003c/p\u003e\n"],["\u003cp\u003eTo use the Multi Table source, you will need to utilize one of the compatible multi table sinks, either BigQuery Multi Table or GCS Multi File.\u003c/p\u003e\n"],["\u003cp\u003eThe process involves enabling APIs, creating a Cloud Data Fusion instance, securely storing your SQL Server password, getting the appropriate JDBC driver, and deploying multiple table plugins.\u003c/p\u003e\n"],["\u003cp\u003eConnecting to SQL Server and the chosen sink (BigQuery or Cloud Storage) is done through the Cloud Data Fusion Studio, and the guide provides steps to run a preview and deploy the pipeline.\u003c/p\u003e\n"]]],[],null,["# Read from multiple Microsoft SQL Server tables\n\n*** ** * ** ***\n\nThis page describes how to read multiple tables from a Microsoft SQL Server\ndatabase, using the **Multi Table** [source](/data-fusion/docs/concepts/overview#source).\nUse the Multi Table source when you want your pipeline to read from\nmultiple tables. If you want your pipeline to read from a single table, see\n[Reading from a SQL Server table](/data-fusion/docs/how-to/reading-from-sqlserver).\n\nThe Multi Table source outputs data with multiple schemas and includes a\ntable name field that indicates the table from which the data came. When\nusing the Multi Table source, use one of the multi table [sinks](/data-fusion/docs/concepts/overview#sink),\n**BigQuery Multi Table** or **GCS Multi File**.\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, Cloud Storage, BigQuery, 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, Cloud Storage, BigQuery, 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, Cloud Storage, BigQuery, 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. Ensure that your SQL Server database can accept connections from Cloud Data Fusion. To do this securely, we recommend that you [create a private\n Cloud Data Fusion instance](/data-fusion/docs/how-to/create-private-ip).\n\n### View your Cloud Data Fusion instance\n\nWhen using Cloud Data Fusion, you use both the Google Cloud console\nand the separate Cloud Data Fusion UI. In the Google Cloud console, you\ncan create a Google Cloud project, and create and delete\nCloud Data Fusion instances. In the Cloud Data Fusion UI, you can use\nthe various pages, such as **Studio** or **Wrangler**, to use\nCloud Data Fusion features.\n\n1. In the Google Cloud console, go to the Cloud Data Fusion page.\n\n2. To open the instance in the Cloud Data Fusion Studio,\n click **Instances** , and then click **View instance**.\n\n[Go to Instances](https://console.cloud.google.com/data-fusion/locations/-/instances) \n\nStore your SQL Server password as a secure key\n----------------------------------------------\n\nAdd your SQL Server password as a secure key to encrypt on your\nCloud Data Fusion instance. Later in this guide, you will ensure that\nyour password is retrieved using [Cloud KMS](/kms/docs).\n\n1. In the top-right corner of any Cloud Data Fusion page, click **System\n Admin**.\n\n2. Click the **Configuration** tab.\n\n3. Click **Make HTTP Calls**.\n\n \u003cbr /\u003e\n\n4. In the dropdown menu, choose **PUT**.\n\n5. In the path field, enter `namespaces/`\u003cvar translate=\"no\"\u003eNAMESPACE_ID\u003c/var\u003e`/securekeys/`\u003cvar translate=\"no\"\u003ePASSWORD\u003c/var\u003e.\n\n6. In the **Body** field, enter `{\"data\":\"`\u003cvar translate=\"no\"\u003eSQL_SERVER_PASSWORD\u003c/var\u003e`\"}`.\n\n7. Click **Send**.\n\nEnsure that the **Response** you get is status code `200`.\n\nGet the JDBC driver for SQL Server\n----------------------------------\n\n### Using the Hub\n\n1. In the Cloud Data Fusion UI, click **Hub**.\n\n2. In the search bar, enter `Microsoft SQL Server JDBC Driver`.\n\n3. Click **Microsoft SQL Server JDBC Driver**.\n\n4. Click **Download**. Follow the download steps shown.\n\n5. Click **Deploy**. Upload the JAR file from the previous step.\n\n6. Click **Finish**.\n\n### Using Studio\n\n1. Visit [Microsoft.com](https://www.microsoft.com/en-us/download/details.aspx?id=11774).\n\n2. Choose your download and click **Download**.\n\n3. In the Cloud Data Fusion UI, click menu\n **Menu** and navigate to the **Studio** page.\n\n4. Click add **Add**.\n\n5. Under **Driver** , click **Upload**.\n\n6. Upload the JAR file downloaded in step 2.\n\n7. Click **Next**.\n\n8. Configure the driver by entering a **Name**.\n\n9. In the **Class name** field, enter `com.microsoft.sqlserver.jdbc.SQLServerDriver`.\n\n10. Click **Finish**.\n\nDeploy the Multiple Table Plugins\n---------------------------------\n\n1. In the Cloud Data Fusion web UI, click **Hub**.\n\n2. In the search bar, enter `Multiple table plugins`.\n\n3. Click **Multiple Table Plugins**.\n\n4. Click **Deploy**.\n\n5. Click **Finish**.\n\n6. Click **Create a Pipeline**.\n\nConnect to SQL Server\n---------------------\n\n1. In the Cloud Data Fusion UI, click menu\n **Menu** and navigate to the **Studio** page.\n\n2. In **Studio** , expand the **Source** menu.\n\n3. Click **Multiple Database Tables**.\n\n4. Hold the pointer over the **Multiple Database Tables** node and click\n **Properties**.\n\n5. In the **Reference name** field, specify a reference name that will be used to\n identify your SQL Server source.\n\n6. In the **JDBC Connection String** field, enter the JDBC connection string. For\n example, `jdbc:sqlserver://mydbhost:1433`. For more information, see\n [Building the connection URL](https://docs.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url).\n\n7. Enter the **JDBC Plugin Name** , **Database User Name** , and\n **Database User Password**.\n\n8. Click **Validate**.\n\n9. Click close **Close**.\n\nConnect to BigQuery or Cloud Storage\n------------------------------------\n\n1. In the Cloud Data Fusion UI, click menu\n **Menu** and navigate to the **Studio** page.\n\n2. Expand **Sink**.\n\n3. Click **BigQuery Multi Table** or **GCS Multi File**.\n\n4. Connect the **Multiple Database Tables** node with **BigQuery Multi Table**\n or **GCS Multi File**.\n\n5. Hold the pointer over the **BigQuery Multi Table**\n or **GCS Multi File** node, click **Properties**, and configure the sink.\n\n For more information, see [Google BigQuery Multi Table Sink](https://cdap.atlassian.net/wiki/spaces/DOCS/pages/464912385/Google+BigQuery+Multi+Table+Sink) and [Google Cloud Storage Multi File Sink](https://cdap.atlassian.net/wiki/spaces/DOCS/pages/464945223/Google+Cloud+Storage+Multi+File+Sink).\n6. Click **Validate**.\n\n7. Click close **Close**.\n\nRun preview of the pipeline\n---------------------------\n\n1. In the Cloud Data Fusion UI, click menu\n **Menu** and navigate to the **Studio** page.\n\n2. Click **Preview**.\n\n3. Click **Run**. Wait for the preview to finish successfully.\n\nDeploy the pipeline\n-------------------\n\n1. In the Cloud Data Fusion UI, click menu\n **Menu** and navigate to the **Studio** page.\n\n2. Click **Deploy**.\n\nRun the pipeline\n----------------\n\n1. In the Cloud Data Fusion UI,\n click menu **Menu**.\n\n2. Click **List**.\n\n3. Click the pipeline.\n\n4. On the pipeline details page, click **Run**.\n\nWhat's next\n-----------\n\n- Learn more about [Cloud Data Fusion](/data-fusion/docs/concepts/overview).\n- Follow one of the [tutorials](/data-fusion/docs/tutorials)."]]
