How to integrate Google BigQuery using SSIS

Learn how to quickly and efficiently connect Google BigQuery with SSIS for smooth data access.

Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required. You can do it all using the high-performance Google BigQuery Connector for SSIS. We'll walk you through the entire setup.

Ready to dive in? Download the product to jump right in, or follow the step-by-step guide below to see how it works.

Video Tutorial

This video covers the following topics and more, so please watch carefully. After watching the video, follow the steps outlined in this article:

• How to download and install the required PowerPack for Google BigQuery integration in SSIS
• How to configure the connection for Google BigQuery
• Features of the ZappySys API Source (Authentication / Query Language / Examples / Driver UI)
• How to use the Google BigQuery in SSIS

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!).

Read data from Google BigQuery in SSIS (Export data)

In this section we will learn how to configure and use Google BigQuery Connector in API Source to extract data from Google BigQuery.

1. Begin with opening Visual Studio and Create a New Project.

2. Select Integration Service Project and in new project window set the appropriate name and location for project. And click OK.

   In the new SSIS project screen you will find the following:

   • SSIS ToolBox on left side bar
   • Solution Explorer and Property Window on right bar
   • Control flow, data flow, event Handlers, Package Explorer in tab windows
   • Connection Manager Window in the bottom
   
   Note: If you don't see ZappySys SSIS PowerPack Task or Components in SSIS Toolbox, please refer to this help link.

3. Now, Drag and Drop SSIS Data Flow Task from SSIS Toolbox. Double click on the Data Flow Task to see Data Flow designer.

   

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

   

5. Select New Connection to create a new connection:
   

   

6. 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

   

7. Now it's time to configure authentication. Firstly, configure authentication settings in Google BigQuery service and then proceed by configuring API Connection Manager. Start by expanding an authentication type:

   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)

   

   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)

   

8. Select the desired endpoint, change/pass the properties values, and click on Preview Data button to make the API call.

   API Source - Google BigQuery

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

   

9. 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)

   

Write data to Google BigQuery using SSIS (Import data)

In this section we will learn how to configure and use Google BigQuery Connector in the API Destination to write data to Google BigQuery.

Video tutorial

This video covers following and more so watch carefully. After watching this video follow the steps described in this article.

• How to download SSIS PowerPack for Google BigQuery integration in SSIS
• How to configure connection for Google BigQuery
• How to write or lookup data to Google BigQuery
• Features about SSIS API Destination
• Using Google BigQuery Connector in SSIS

Step-by-step instructions

In upper section we learned how to read data, now in this section we will learn how to configure Google BigQuery in the API Source to POST data to the Google BigQuery.

1. Begin with opening Visual Studio and Create a New Project.

2. Select Integration Service Project and in new project window set the appropriate name and location for project. And click OK.

   In the new SSIS project screen you will find the following:

   • SSIS ToolBox on left side bar
   • Solution Explorer and Property Window on right bar
   • Control flow, data flow, event Handlers, Package Explorer in tab windows
   • Connection Manager Window in the bottom
   
   Note: If you don't see ZappySys SSIS PowerPack Task or Components in SSIS Toolbox, please refer to this help link.

3. Now, Drag and Drop SSIS Data Flow Task from SSIS Toolbox. Double click on the Data Flow Task to see Data Flow designer.

   

4. Read the data from the source, using any desired source component. You can even make an API call using the ZappySys JSON/XML/API Source and read data from there. In this example, we will use an OLE DB Source component to read real-time data from a SQL Server database.

5. From the SSIS Toolbox drag and drop API Destination (Predefined Templates) on the Data Flow Designer surface and connect source component with it, and double click to edit it.
   

6. Select New Connection to create a new connection:
   

   API Destination - Google BigQuery

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

   

7. To configure the Google BigQuery connector, choose one of the following methods:

   • Choose from Popular Connector List: Select a pre-installed service directly from the dropdown menu.
   • Search Online: Use this to find and download a new connector file to your computer.
   • Use Saved/Downloaded File: Once the file is downloaded, browse your local drive to load it into the configuration.

   After that, just click Continue >>:

   Google BigQuery

   

8. Proceed with selecting the desired Authentication Type. Then select API Base URL (in most cases default one is the right one). Finally, fill in all the required parameters and set optional parameters if needed. You may press a link Steps to Configure which will help set certain parameters. More info is available in Authentication section.

   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)

   

   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)

   

9. Select the desired endpoint, change/pass the properties values, and go to the Mappings tab to map the columns.

   API Destination - Google BigQuery

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

   

10. Finally, map the desired columns:

    API Destination - Google BigQuery

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

    

11. That's it; we successfully configured the POST API Call. In a few clicks we configured the Google BigQuery API call using ZappySys Google BigQuery Connector

    

Load Google BigQuery data into SQL Server using Upsert Destination (Insert or Update)

Once you configured the data source, you can load Google BigQuery data into SQL Server using Upsert Destination.

Upsert Destination can merge or synchronize source data with the target table. It supports Microsoft SQL Server, PostgreSQL, and Redshift databases as targets. Upsert Destination also supports very fast bulk upsert operation along with bulk delete.

1. From the SSIS Toolbox drag-and-drop Upsert Destination component onto the Data Flow designer background.

2. Connect your SSIS source component to Upsert Destination.

3. Double-click on Upsert Destination component to open configuration window.

4. Start by selecting the Action from the list.

5. Next, select the desired target connection or create one by clicking <New [provider] Connection> menu item from the Target Connection dropdown.

6. Then select a table from the Target Table list or click New button to create a new table based on the source columns.

7. Continue by checking Insert and Update options according to your scenario (e.g. if Update option is unchecked, no updates will be made).

8. Finally, click Map All button to map all columns and then select the Key columns to match the columns on:

   

9. Click OK to save the configuration.

10. Run the package and Google BigQuery data will be merged with the target table in SQL Server, PostgreSQL, or Redshift:

    

11. Done!

Deploy and schedule SSIS package

After you are done creating SSIS package, most likely, you want to deploy it to SQL Server Catalog and run it periodically. Just follow the instructions in this article:

• How to design, debug, deploy, schedule SSIS Package (In SQL Agent and Catalog)

Running SSIS package in Azure Data Factory (ADF)

To use SSIS PowerPack in ADF, you must first prepare Azure-SSIS Integration Runtime. Follow this link for detailed instructions:

• Preparing ADF instance to use SSIS PowerPack

Supported Google BigQuery Connector actions

Got a specific use case in mind? We've mapped out exactly how to perform a variety of essential Google BigQuery operations directly in SSIS, so you don't have to figure out the setup from scratch. Check out the step-by-step guides below:

• [Dynamic Endpoint]
• Create Dataset
• Delete Dataset
• Delete Table
• Get Query Schema (From SQL)
• Get Table Schema
• 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)

Conclusion

In this article we showed you how to connect to Google BigQuery in SSIS and integrate data without writing complex code — all of this was powered by Google BigQuery Connector.

Download SSIS PowerPack 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):

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