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Questa pagina descrive come leggere più tabelle da un database Microsoft SQL Server utilizzando l'origineMulti Table.
Utilizza l'origine Multi Table quando vuoi che la pipeline legga da più tabelle. Se vuoi che la pipeline legga da una singola tabella, vedi
Lettura da una tabella SQL Server.
L'origine Multi Table restituisce dati con più schemi e include un campo
nome tabella che indica la tabella da cui provengono i dati. Quando
utilizzi l'origine Multi Table, utilizza uno dei sink multi-tabella,
BigQuery Multi Table o GCS Multi File.
Prima di iniziare
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.
Assicurati che il database SQL Server possa accettare connessioni da
Cloud Data Fusion. Per farlo in modo sicuro, ti consigliamo di
creare un'istanza Cloud Data Fusion privata.
Visualizzare l'istanza Cloud Data Fusion
Quando utilizzi Cloud Data Fusion, usi sia la console Google Cloud sia la UI di Cloud Data Fusion separata. Nella console Google Cloud , puoi creare un progetto Google Cloud e creare ed eliminare istanze Cloud Data Fusion. Nella UI di Cloud Data Fusion, puoi utilizzare
le varie pagine, come Studio o Wrangler, per utilizzare
le funzionalità di Cloud Data Fusion.
Nella Google Cloud console, vai alla pagina Cloud Data Fusion.
Per aprire l'istanza in Cloud Data Fusion Studio,
fai clic su Istanze e poi su Visualizza istanza.
Archivia la password di SQL Server come chiave sicura
Aggiungi la password di SQL Server come chiave sicura per la crittografia
nell'istanza Cloud Data Fusion. Più avanti in questa guida, ti assicurerai che
la password venga recuperata utilizzando Cloud KMS.
Nell'angolo in alto a destra di qualsiasi pagina di Cloud Data Fusion, fai clic su Amministratore
di sistema.
Fai clic sulla scheda Configuration (Configurazione).
Fai clic su Effettua chiamate HTTP.
Nel menu a discesa, scegli PUT.
Nel campo del percorso, inserisci namespaces/NAMESPACE_ID/securekeys/PASSWORD.
Nel campo Corpo, inserisci {"data":"SQL_SERVER_PASSWORD"}.
Fai clic su Invia.
Assicurati che la Risposta che ricevi sia il codice di stato 200.
Ottieni il driver JDBC per SQL Server
Utilizzare l'hub
Nella UI di Cloud Data Fusion, fai clic su Hub.
Nella barra di ricerca, inserisci Microsoft SQL Server JDBC Driver.
Fai clic su Driver JDBC di Microsoft SQL Server.
Fai clic su Scarica. Segui i passaggi di download mostrati.
Fai clic su Esegui il deployment. Carica il file JAR del passaggio precedente.
Nella UI di Cloud Data Fusion, fai clic su menuMenu e vai alla pagina Studio.
Fai clic su addAggiungi.
Nella sezione Driver, fai clic su Carica.
Carica il file JAR scaricato nel passaggio 2.
Fai clic su Avanti.
Configura il driver inserendo un nome.
Nel campo Nome classe, inserisci com.microsoft.sqlserver.jdbc.SQLServerDriver.
Fai clic su Fine.
Esegui il deployment dei plug-in per più tabelle
Nella UI web di Cloud Data Fusion, fai clic su Hub.
Nella barra di ricerca, inserisci Multiple table plugins.
Fai clic su Multiple Table Plugins (Plug-in per più tabelle).
Fai clic su Esegui il deployment.
Fai clic su Fine.
Fai clic su Crea una pipeline.
Connettiti a SQL Server
Nella UI di Cloud Data Fusion, fai clic su menuMenu e vai alla pagina Studio.
In Studio, espandi il menu Origine.
Fai clic su Multiple Database Tables (Tabelle di più database).
Tieni il puntatore sul nodo Multiple Database Tables (Più tabelle di database) e fai clic su
Properties (Proprietà).
Nel campo Nome di riferimento, specifica un nome di riferimento che verrà utilizzato per
identificare l'origine SQL Server.
Nel campo Stringa di connessione JDBC, inserisci la stringa di connessione JDBC. Ad esempio, jdbc:sqlserver://mydbhost:1433. Per saperne di più, consulta la sezione
Creazione dell'URL di connessione.
Inserisci Nome plug-in JDBC, Nome utente database e
Password utente database.
Fai clic su Validate (Convalida).
Fai clic su closeClose (Chiudi).
Connettersi a BigQuery o Cloud Storage
Nella UI di Cloud Data Fusion, fai clic su menuMenu e vai alla pagina Studio.
Espandi Sink.
Fai clic su BigQuery Multi Table o GCS Multi File.
Collega il nodo Multiple Database Tables a BigQuery Multi Table
o GCS Multi File.
Tieni il puntatore sul nodo BigQuery Multi Table
o GCS Multi File, fai clic su Properties (Proprietà) e configura il sink.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 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)."]]
