How to integrate Google BigQuery with SSRS

Learn how to quickly and efficiently connect Google BigQuery with SSRS 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 ODBC Driver for SSRS (often referred to as the Google BigQuery Connector). 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 SSRS
• How to configure the connection for Google BigQuery
• Features of the ZappySys API Driver (Authentication / Query Language / Examples / Driver UI)
• How to use the Google BigQuery in SSRS

Create data source in ZappySys Data Gateway

In this section we will create a data source for Google BigQuery in the Data Gateway. Let's follow these steps to accomplish that:

1. Download and install ODBC PowerPack (if you haven't already).

2. Search for gateway in the Windows Start Menu and open ZappySys Data Gateway Configuration:

   

3. Go to the Users tab and follow these steps to add a Data Gateway user:

   • Click the Add button
   • In the Login field enter a username, e.g., john
   • Then enter a Password
   • Check the Is Administrator checkbox
   • Click OK to save
   

4. Now we are ready to add a data source:

   • Click the Add button
   • Give the Data source a name (have it handy for later)
   • Then select Native - ZappySys API Driver
   • Finally, click OK
   GoogleBigqueryDSN

   ZappySys API Driver

   

5. When the Configuration window appears give your data source a name if you haven't done that already, then select "Google BigQuery" from the list of Popular Connectors. If "Google BigQuery" is not present in the list, then click "Search Online" and download it. Then set the path to the location where you downloaded it. Finally, click Continue >> to proceed with configuring the DSN:

   GoogleBigqueryDSN

   Google BigQuery

   

6. Now it's time to configure the Connection Manager. Select Authentication Type, e.g. Token Authentication. Then select API Base URL (in most cases, the default one is the right one). More info is available in the 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:

   GoogleBigqueryDSN

   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:

   GoogleBigqueryDSN

   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)

   

7. Once the data source connection has been configured, it's time to configure the SQL query. Select the Preview tab and then click Query Builder button to configure the SQL query:

   

   ZappySys API Driver - Google BigQuery

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

   GoogleBigqueryDSN

   

8. Start by selecting the Table or Endpoint you are interested in and then configure the parameters. This will generate a query that we will use in SSRS to retrieve data from Google BigQuery. Hit OK button to use this query in the next step.

   #DirectSQL
   SELECT *
   FROM bigquery-public-data.samples.wikipedia
   LIMIT 1000

   
   Some parameters configured in this window will be passed to the Google BigQuery API, e.g. filtering parameters. It means that filtering will be done on the server side (instead of the client side), enabling you to get only the meaningful data much faster.

9. Now hit Preview Data button to preview the data using the generated SQL query. If you are satisfied with the result, use this query in SSRS:

   

   ZappySys API Driver - Google BigQuery

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

   GoogleBigqueryDSN

   #DirectSQL
   SELECT *
   FROM bigquery-public-data.samples.wikipedia
   LIMIT 1000

   
   You can also access data quickly from the tables dropdown by selecting <Select table>.
   A WHERE clause, LIMIT keyword will be performed on the client side, meaning that the whole result set will be retrieved from the Google BigQuery API first, and only then the filtering will be applied to the data. If possible, it is recommended to use parameters in Query Builder to filter the data on the server side (in Google BigQuery servers).

10. Click OK to finish creating the data source.

11. Once done, go to the Network Settings tab and Add a firewall rule for inbound traffic:

    
    • This will initially allow all inbound traffic.
    • Click Edit IP filters to restrict access to specific IP addresses or ranges.

12. Crucial Step: After creating or modifying the data source, you must:

    • Click the Save button to persist your changes.
    • Hit Yes when prompted to restart the Data Gateway service.

    This ensures all changes are properly applied:

    
    Skipping this step may cause the new settings to fail, preventing you from connecting to the data source.

Read data in SSRS from ZappySys Data Gateway

1. Open Visual Studio and create a new SSRS project.

2. Then add a new Shared Data Source (you can create a non-shared data source inside report too):

   

3. Continue with creating the Shared Data Source. Select Microsoft SQL Server as Type and hit Build button to proceed further:

   

4. Once a window opens, configure it similarly. Configure "GoogleBigqueryDSN" as database name. Finally, hit Test Connection and OK:

   GoogleBigqueryDSN

   

5. Another window opens, and it should look similarly to this one below which ends the creation of a Data Source:

   DataSource=localhost,5000;Initial Catalog=GoogleBigqueryDSN

   

6. Now it's time to create a Dataset. If you don't have a report created, in one of the wizard's steps it will look like this:

   #DirectSQL SELECT * FROM bigquery-public-data.samples.wikipedia LIMIT 1000

   

7. Finally, once you complete the report, similar results will show up:

   

Passing Parameters to SSRS Report / Filter data

If you want to parameterize your report, then refer to this article

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 SSRS, 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 SSRS and integrate data without writing complex code — all of this was powered by Google BigQuery ODBC Driver.

Download ODBC 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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