Google BigQuery Connector for SSIS : Get table schema

Learn how to get table schema using the Google BigQuery Connector for SSIS. This connector enables you to read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required. We'll walk you through the exact setup.

Let's dive in!

Video tutorial

Watch this quick video to see the integration in action. It walks you through the end-to-end setup, including:

• Installing the SSIS PowerPack
• Configuring a secure connection to Google BigQuery
• Working with Google BigQuery data directly inside SSIS
• Exploring advanced API Source features

Once you are done watching, simply follow the step-by-step written guide below to configure your data source.

Prerequisites

Before we begin, make sure the following prerequisites are met:

1. SSIS designer installed. Sometimes it is referred as BIDS or SSDT (download it from Microsoft).
2. Basic knowledge of SSIS package development using Microsoft SQL Server Integration Services.
3. SSIS PowerPack is installed (if you are new to SSIS PowerPack, then get started!).

Get table schema in SSIS

1. Open Visual Studio and click Create a new project.

2. Select Integration Services Project. Enter a name and location for your project, then click OK.

3. From the SSIS Toolbox, drag and drop a Data Flow Task onto the Control Flow surface, and double-click it:

   

4. Make sure you are in the Data Flow Task designer:

   

5. From the SSIS toolbox drag and API Source (Predefined Templates) on the data flow designer surface, and double click on it to edit it:
   

   

6. Select New Connection to create a new connection:
   

   

7. Use a preinstalled Google BigQuery Connector from Popular Connector List or press Search Online radio button to download Google BigQuery Connector. Once downloaded simply use it in the configuration:
   

   Google BigQuery

   

8. Select your authentication scenario below to expand connection configuration steps to:

   • Configure the authentication in Google BigQuery.
   • Enter those details into the API Connection Manager configuration.

   User Account

   Google BigQuery authentication

   User accounts represent a developer, administrator, or any other person who interacts with Google APIs and services. User accounts are managed as Google Accounts, either with Google Workspace or Cloud Identity. They can also be user accounts that are managed by a third-party identity provider and federated with Workforce Identity Federation. [API reference]

   Follow these steps on how to create Client Credentials (User Account principle) to authenticate and access BigQuery API in SSIS package or ODBC data source:

   WARNING: If you are planning to automate processes, we recommend that you use a Service Account authentication method. In case, you still need to use User Account, then make sure you use a system/generic account (e.g. automation@my-company.com). When you use a personal account which is tied to a specific employee profile and that employee leaves the company, the token may become invalid and any automated processes using that token will start to fail.

   Step-1: Create project

   This step is optional, if you already have a project in Google Cloud and can use it. However, if you don't, proceed with these simple steps to create one:

   1. First of all, go to Google API Console.

   2. Then click Select a project button and then click NEW PROJECT button:

      

   3. Name your project and click CREATE button:

      

   4. Wait until the project is created:

      
   5. Done! Let's proceed to the next step.

   Step-2: Enable Google Cloud APIs

   In this step we will enable BigQuery API and Cloud Resource Manager API:

   1. Select your project on the top bar:

      

   2. Then click the "hamburger" icon on the top left and access APIs & Services:

      

   3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      

   4. In the search bar search for bigquery api and then locate and select BigQuery API:

      

   5. If BigQuery API is not enabled, enable it:

      

   6. Then repeat the step and enable Cloud Resource Manager API as well:

      
   7. Done! Let's proceed to the next step.

   Step-3: Create OAuth application

   1. First of all, click the "hamburger" icon on the top left and then hit VIEW ALL PRODUCTS:

      

   2. Then access Google Auth Platform to start creating an OAuth application:

      

   3. Start by pressing GET STARTED button:

      

   4. Next, continue by filling in App name and User support email fields:

      

   5. Choose Internal option, if it's enabled, otherwise select External:

      

   6. Optional step if you used Internal option in the previous step. Nevertheless, if you had to use External option, then click ADD USERS to add a user:

      

   7. Then add your contact Email address:

      

   8. Finally, check the checkbox and click CREATE button:

      
   9. Done! Let's create Client Credentials in the next step.

   Step-4: Create Client Credentials

   1. In Google Auth Platform, select Clients menu item and click CREATE CLIENT button:

      

   2. Choose Desktop app as Application type and name your credentials:

      

   3. Continue by opening the created credentials:

      

   4. Finally, copy Client ID and Client secret for the later step:

      

   5. Done! We have all the data needed for authentication, let's proceed to the last step!

   Step-5: Configure connection

   1. Now go to SSIS package or ODBC data source and use previously copied values in User Account authentication configuration:

      • In the ClientId field paste the Client ID value.
      • In the ClientSecret field paste the Client secret value.

   2. Press Generate Token button to generate Access and Refresh Tokens.

   3. Then choose ProjectId from the drop down menu.

   4. Continue by choosing DatasetId from the drop down menu.

   5. Finally, click Test Connection to confirm the connection is working.

   6. Done! Now you are ready to use Google BigQuery Connector!

   API Connection Manager configuration

   Just perform these simple steps to finish authentication configuration:

   1. Set Authentication Type to User Account [OAuth]
   2. Optional step. Modify API Base URL if needed (in most cases default will work).
   3. Fill in all the required parameters and set optional parameters if needed.
   4. Press Generate Token button to generate the tokens.
   5. Finally, hit OK button:

   Google BigQuery

   User Account [OAuth]

   https://www.googleapis.com/bigquery/v2

   Required Parameters
   UseCustomApp	Fill-in the parameter...
   ProjectId (Choose after [Generate Token] clicked)	Fill-in the parameter...
   DatasetId (Choose after [Generate Token] clicked and ProjectId selected)	Fill-in the parameter...
   Optional Parameters
   ClientId
   ClientSecret
   Scope	https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write
   RetryMode	RetryWhenStatusCodeMatch
   RetryStatusCodeList	429|503
   RetryCountMax	5
   RetryMultiplyWaitTime	True
   Job Location
   Redirect URL (Only for Web App)

   

   Find full details in the Google BigQuery Connector authentication reference.

   Service Account (Using *.json OR *.p12 key file)

   Google BigQuery authentication

   Service accounts are accounts that do not represent a human user. They provide a way to manage authentication and authorization when a human is not directly involved, such as when an application needs to access Google Cloud resources. Service accounts are managed by IAM. [API reference]

   Follow these steps on how to create Service Account to authenticate and access BigQuery API in SSIS package or ODBC data source:

   Step-1: Create project

   This step is optional, if you already have a project in Google Cloud and can use it. However, if you don't, proceed with these simple steps to create one:

   1. First of all, go to Google API Console.

   2. Then click Select a project button and then click NEW PROJECT button:

      

   3. Name your project and click CREATE button:

      

   4. Wait until the project is created:

      
   5. Done! Let's proceed to the next step.

   Step-2: Enable Google Cloud APIs

   In this step we will enable BigQuery API and Cloud Resource Manager API:

   1. Select your project on the top bar:

      

   2. Then click the "hamburger" icon on the top left and access APIs & Services:

      

   3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      

   4. In the search bar search for bigquery api and then locate and select BigQuery API:

      

   5. If BigQuery API is not enabled, enable it:

      

   6. Then repeat the step and enable Cloud Resource Manager API as well:

      
   7. Done! Let's proceed to the next step and create a service account.

   Step-3: Create Service Account

   Use the steps below to create a Service Account in Google Cloud:

   1. First of all, go to IAM & Admin in Google Cloud console:

      

   2. Once you do that, click Service Accounts on the left side and click CREATE SERVICE ACCOUNT button:

      

   3. Then name your service account and click CREATE AND CONTINUE button:

      

   4. Continue by clicking Select a role dropdown and start granting service account BigQuery Admin and Project Viewer roles:

      

   5. Find BigQuery group on the left and then click on BigQuery Admin role on the right:

      

   6. Then click ADD ANOTHER ROLE button, find Project group and select Viewer role:

      

   7. Finish adding roles by clicking CONTINUE button:

      
      You can always add or modify permissions later in IAM & Admin.

   8. Finally, in the last step, just click button DONE:

      

   9. Done! We are ready to add a Key to this service account in the next step.

   Step-4: Add Key to Service Account

   We are ready to add a Key (JSON or P12 key file) to the created Service Account:

   1. In Service Accounts open newly created service account:

      

   2. Next, copy email address of your service account for the later step:

      

   3. Continue by selecting KEYS tab, then press ADD KEY dropdown, and click Create new key menu item:

      

   4. Finally, select JSON (Engine v19+) or P12 option and hit CREATE button:

      
   5. Key file downloads into your machine. We have all the data needed for authentication, let's proceed to the last step!

   Step-5: Configure connection

   1. Now go to SSIS package or ODBC data source and configure these fields in Service Account authentication configuration:

      • In the Service Account Email field paste the service account Email address value you copied in the previous step.
      • In the Service Account Private Key Path (i.e. *.json OR *.p12) field use downloaded certificate's file path.
   2. Done! Now you are ready to use Google BigQuery Connector!

   API Connection Manager configuration

   Just perform these simple steps to finish authentication configuration:

   1. Set Authentication Type to Service Account (Using *.json OR *.p12 key file) [OAuth]
   2. Optional step. Modify API Base URL if needed (in most cases default will work).
   3. Fill in all the required parameters and set optional parameters if needed.
   4. Finally, hit OK button:

   Google BigQuery

   Service Account (Using *.json OR *.p12 key file) [OAuth]

   https://www.googleapis.com/bigquery/v2

   Required Parameters
   Service Account Email	Fill-in the parameter...
   Service Account Private Key Path (i.e. *.json OR *.p12)	Fill-in the parameter...
   ProjectId	Fill-in the parameter...
   DatasetId (Choose after ProjectId)	Fill-in the parameter...
   Optional Parameters
   Scope	https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write
   RetryMode	RetryWhenStatusCodeMatch
   RetryStatusCodeList	429
   RetryCountMax	5
   RetryMultiplyWaitTime	True
   Job Location
   Impersonate As (Enter Email Id)

   

   Find full details in the Google BigQuery Connector authentication reference.

9. Select Get Table Schema endpoint from the dropdown and hit Preview Data:

   

   API Source - Google BigQuery

   Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required.

   Google BigQuery

   Get Table Schema

   Required Parameters
   DatasetId	Fill-in the parameter...
   TableId	Fill-in the parameter...
   Filter	Fill-in the parameter...

   

10. That's it! We are done! Just in a few clicks we configured the call to Google BigQuery using Google BigQuery Connector.

    You can load the source data into your desired destination using the Upsert Destination , which supports SQL Server, PostgreSQL, and Amazon Redshift. We also offer other destinations such as CSV , Excel , Azure Table , Salesforce , and more . You can check out our SSIS PowerPack Tasks and components for more options. (*loaded in Trash Destination)

    

Conclusion

And there you have it — a complete guide on how to get table schema in SSIS without writing complex code. All of this was powered by Google BigQuery Connector, which handled the REST API pagination and authentication for us automatically.

Download the trial now or ping us via chat if you have any questions or are looking for a specific feature (you can also reach out to us by submitting a ticket):

More actions supported by Google BigQuery Connector

Got another use case in mind? We've documented the exact setups for a variety of essential Google BigQuery operations directly in SSIS, so you can skip the trial and error. Find your next step-by-step guide below:

• [Dynamic Endpoint]
• Create Dataset
• Delete Dataset
• Delete Table
• Get Query Schema (From SQL)
• Insert Table Data
• List Datasets
• List Projects
• List Tables
• Post Dynamic Endpoint
• Read Data using SQL Query -OR- Execute Script (i.e. CREATE, SELECT, INSERT, UPDATE, DELETE)
• Read Table Rows
• Make Generic REST API Request
• Make Generic REST API Request (Bulk Write)

More Google BigQuery integrations

SQL Server

Microsoft Fabric

Power BI

SSRS

Informatica PowerCenter

MS Access

Excel

SSAS

C#

Python

Java

Tableau

SAP Crystal Reports

Azure Data Factory (Pipeline)

Azure Data Factory (SSIS)

Talend Studio

UiPath

PowerShell

Alteryx

ODBC