Google BigQuery Connector for Power BI Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector In this article you will learn how to quickly and efficiently integrate Google BigQuery data in Power BI without coding. We will use high-performance Google BigQuery Connector to easily connect to Google BigQuery and then access the data inside Power BI. Let's follow the steps below to see how we can accomplish that! Download Documentation Google BigQuery Connector for Power BI is based on ZappySys API Driver which is part of ODBC PowerPack. It is a collection of high-performance ODBC drivers that enable you to integrate data in SQL Server, SSIS, a programming language, or any other ODBC-compatible application. ODBC PowerPack supports various file formats, sources and destinations, including REST/SOAP API, SFTP/FTP, storage services, and plain files, to mention a few.

Connect to Google BigQuery in other apps Connect to Google BigQuery in SSIS Connect to Google BigQuery in SQL Server Connect to Google BigQuery in SSRS Connect to Google BigQuery in Informatica Connect to Google BigQuery in MS Access Connect to Google BigQuery in MS Excel Connect to Google BigQuery in SSAS Connect to Google BigQuery in C# Connect to Google BigQuery in Python Connect to Google BigQuery in JAVA Connect to Google BigQuery in Tableau Connect to Google BigQuery in SAP Crystal Reports Connect to Google BigQuery in Azure Data Factory (Pipeline) Connect to Google BigQuery in Azure Data Factory (SSIS) Connect to Google BigQuery in Talend Studio Connect to Google BigQuery in UiPath Connect to Google BigQuery in PowerShell Connect to Google BigQuery in ODBC

Video Tutorial - Integrate Google BigQuery data in Power BI

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 Power BI
• 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 Power BI

Create ODBC Data Source (DSN) based on ZappySys API Driver

Step-by-step instructions

To get data from Google BigQuery using Power BI we first need to create a DSN (Data Source) which will access data from Google BigQuery. We will later be able to read data using Power BI. Perform these steps:

1. Download and install ODBC PowerPack.

2. Open ODBC Data Sources (x64):

   

3. Create a User data source (User DSN) based on ZappySys API Driver

   ZappySys API Driver

   
   • Create and use User DSN if the client application is run under a User Account. This is an ideal option in design-time, when developing a solution, e.g. in Visual Studio 2019. Use it for both type of applications - 64-bit and 32-bit.
   • Create and use System DSN if the client application is launched under a System Account, e.g. as a Windows Service. Usually, this is an ideal option to use in a production environment. Use ODBC Data Source Administrator (32-bit), instead of 64-bit version, if Windows Service is a 32-bit application.
   Power BI uses a Service Account, when a solution is deployed to production environment, therefore for production environment you have to create and use a System DSN.

4. 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

   

5. 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 [OAuth]

   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]

   Steps how to get and use Google BigQuery credentials
   

   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!

   Fill in all required parameters and set optional parameters if needed:

   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 [OAuth]

   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]

   Steps how to get and use Google BigQuery credentials
   

   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!

   Fill in all required parameters and set optional parameters if needed:

   GoogleBigqueryDSN

   Google BigQuery

   Service Account [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)

   

6. 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 / write Google BigQuery data inside your app without coding using easy to use high performance API Connector

   GoogleBigqueryDSN

   

7. 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 Power BI 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 /* try your own dataset or Some FREE dataset like nyc-tlc.yellow.trips -- 3 parts ([Project.]Dataset.Table) */

   
   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.

8. 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 Power BI:

   

   ZappySys API Driver - Google BigQuery

   Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector

   GoogleBigqueryDSN

   #DirectSQL SELECT * FROM bigquery-public-data.samples.wikipedia LIMIT 1000 /* try your own dataset or Some FREE dataset like nyc-tlc.yellow.trips -- 3 parts ([Project.]Dataset.Table) */

   
   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).

9. Click OK to finish creating the data source.

Video Tutorial

Read Google BigQuery data in Power BI using ODBC

Importing Google BigQuery data into Power BI from table or view

1. Once you open Power BI Desktop click Get Data to get data from ODBC:
   

2. A window opens, and then search for "odbc" to get data from ODBC data source:
   

3. Another window opens and asks to select a Data Source we already created. Choose GoogleBigqueryDSN and continue:

   GoogleBigqueryDSN

   

4. Most likely, you will be asked to authenticate to a newly created DSN. Just select Windows authentication option together with Use my current credentials option:

   GoogleBigqueryDSN

   

5. Finally, you will be asked to select a table or view to get data from. Select one and load the data!
   

6. Finally, finally, use extracted data from Google BigQuery in a Power BI report:
   

Importing Google BigQuery data into Power BI using SQL query

If you wish to import Google BigQuery data from SQL query rather than a table then you can use advanced options during import steps (as below). After selecting DSN you can click on advanced options to see SQL Query editor.

GoogleBigqueryDSN

#DirectSQL SELECT * FROM bigquery-public-data.samples.wikipedia LIMIT 1000 /* try your own dataset or Some FREE dataset like nyc-tlc.yellow.trips -- 3 parts ([Project.]Dataset.Table) */

Using a full ODBC connection string

In the previous steps we used a very short format of ODBC connection string - a DSN. Yet sometimes you don't want a dependency on an ODBC data source (and an extra step). In those times, you can define a full connection string and skip creating an ODBC data source entirely. Let's see below how to accomplish that in the below steps:

1. Open ODBC data source configuration and click Copy settings:
   

   ZappySys API Driver - Configuration [Version: 2.0.1.10418]

   ZappySys API Driver - Google BigQuery

   Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector

   GoogleBigqueryDSN

   
2. The window opens, telling us the connection string was successfully copied to the clipboard:
3. Then just paste the connection string into your script:
   GoogleBigqueryDSN

   DRIVER={ZappySys API Driver};ServiceUrl=https://www.googleapis.com/bigquery/v2;Provider=GoogleBigQuery;

   
4. You are good to go! The script will execute the same way as using a DSN.

Limitations of using a full connection string

Despite using a full ODBC connection string may be very convenient it comes with a limitation: it's length is limited to 1024 symbols (or sometimes more). It usually happens when API provider generates a very long Refresh Token when OAuth is at play. If you are using such a long ODBC connection string, you may get this error:

"Connection string exceeds maximum allowed length of 1024"

But there is a solution to this by storing the full connection string in a file. Follow the steps below to achieve this:

1. Open your ODBC data source.
2. Click Copy settings button to copy a full connection string (see the previous section on how to accomplish that).
3. Then create a new file, let's say, in C:\temp\odbc-connection-string.txt.
4. Continue by pasting the copied connection string into a newly created file and save it.
5. Finally, the last step! Just construct a shorter ODBC connection string using this format:

   DRIVER={ZappySys API Driver};SettingsFile=C:\temp\odbc-connection-string.txt

6. Our troubles are over! Now you should be able to use this connection string in Power BI with no problems.

Editing query for table in Power BI

There will be a time you need to change the initial query after importing data into Power BI. Don't worry, just right-click on your table and click Edit query menu item:

Using parameters in Power BI (dynamic query)

In the real world, many values of your REST / SOAP API call may be coming from parameters. If that's the case for you can try to edit script manually as below. In below example its calling SQL Query with POST method and passing some parameters. Notice below where paraAPIKey is Power BI Parameter (string type). You can use parameters anywhere in your script just like the normal variable.

To use a parameter in Power BI report, follow these simple steps:

1. Firstly, you need to Edit query of your table (see previous section)

2. Then just create a new parameter by clicking Manage Parameters dropdown, click New Parameter option, and use it in the query:

   						
                               = Odbc.Query("dsn=GoogleBigqueryDSN",
                                            "SELECT ProductID, ProductName, UnitPrice, UnitsInStock
                                             FROM Products
                                             WHERE UnitPrice > " & Text.From(MyParameter) & "
                                             ORDER BY UnitPrice")
                           
                   

   
   Refer to Power Query M reference for more information on how to use its advanced features in your queries.

Using DirectQuery Option rather than Import

So far we have seen how to Import Google BigQuery data into Power BI, but what if you have too much data and you don't want to import but link it. Power BI Offers very useful feature for this scenario. It's called DirectQuery Option. In this section we will explore how to use DirectQuery along with ZappySys Drivers.

Out of the box ZappySys Drivers won't work in ODBC Connection Mode, so you have to use SQL Server Connection rather than ODBC if you wish to use Live data using DirectQuery option. See below step-by-step instructions to enable DirectQuery mode in Power BI for Google BigQuery data.

Basically we will use ZappySys Data Gateway its part of ODBC PowerPack. We will then use Linked Server in SQL Server to Link API Service, then issue OPENROWSET queries from Power BI to SQL Server, and it will then call Google BigQuery via ZappySys Data Gateway.

1. First, create a data source in ZappySys Data Gateway and create a Linked Server based on it.
2. Once SQL Server Linked Server is configured we are ready to issue a SQL query in Power BI.
3. Click Get Data in Power BI, select SQL Server Database
4. Enter your server name and any database name
5. Select Mode as DirectQuery
6. Click on Advanced and enter query like below (we are assuming you have created Google BigQuery Data Source in Data Gateway and defined linked server - change the name below).

   SELECT * FROM OPENQUERY([LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY], '#DirectSQL SELECT * FROM bigquery-public-data.samples.wikipedia LIMIT 1000 /* try your own dataset or Some FREE dataset like nyc-tlc.yellow.trips -- 3 parts ([Project.]Dataset.Table) */')

   SELECT * FROM OPENQUERY([LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY], '#DirectSQL SELECT * FROM bigquery-public-data.samples.wikipedia LIMIT 1000 /* try your own dataset or Some FREE dataset like nyc-tlc.yellow.trips -- 3 parts ([Project.]Dataset.Table) */')

   

   
   DirectQuery option for Power BI (Read Google BigQuery Data Example using SQL Server Linked Server and ZappySys Data Gateway)

7. Click OK and Load data... That's it. Now your Google BigQuery API data is linked rather than imported.

Publishing Power BI report to Power BI service

Here are the instructions on how to publish a Power BI report to Power BI service from Power BI Desktop application:

1. First of all, go to Power BI Desktop, open a Power BI report, and click Publish button:

   

2. Then select the Workspace you want to publish report to and hit Select button:

   

3. Finally, if everything went right, you will see a window indicating success:

   

   If you need to periodically refresh Power BI semantic model (dataset) to ensure data accuracy and up-to-dateness, you can accomplish that by using Microsoft On-premises data gateway. Proceed to the next section - Refreshing Power BI semantic model (dataset) using On-premises data gateway - and learn how to do that.

Refreshing Power BI semantic model (dataset) using On-premises data gateway

Power BI allows to refresh semantic models which are based on data sources that reside on-premises. This can be achieved using Microsoft On-premises data gateway. There are two types of On-premises gateways:

• Standard Mode
• Personal Mode

Standard Mode supports Power BI and other Microsoft Data Fabric services. It fits perfectly for Enterprise solutions as it installs as a Windows Service and also supports Direct Query feature.

Personal Mode, on the other hand, can be configured faster, but is designed more for home users (you cannot install it as a Windows Service and it does not support DirectQuery). You will find a detailed comparison in the link above.

We recommend to go with Personal Mode for a quick POC solution, but use Standard Mode in production environment.

Below you will find instructions on how to refresh semantic model using both types of gateways.

Refresh using On-premises data gateway (standard mode)

Here are the instructions on how to refresh a Power BI semantic model using On-premises data gateway (standard mode):

1. Go to Power BI My workspace, hover your mouse cursor on your semantic model and click Settings:

   

2. If you see this view, it means you have to install On-premises data gateway (standard mode):

   

3. Install On-premises data gateway (standard mode) and sign-in:

   
   Use the same email address you use when logging in into Power BI account.

4. Register a new gateway (or migrate an existing one):

   

5. If you are creating a new gateway, name your gateway, enter a Recovery key, and click Configure button:

   

6. Now, let's get back to your semantic model settings in Power BI portal. Refresh the page and you should see your newly created gateway. Click arrow icon and then click on Add to gateway link:

   ODBC{"connectionstring":"dsn=GoogleBigqueryDSN"}

   

7. Once you do that, you will create a new gateway connection. Give it a name, set Authentication method, Privacy level, and click Create button:

   dsn=GoogleBigqueryDSN

   
   In this example, we used the least restrictive Privacy level.

   If your connection uses a full connection string you may hit a length limitation when entering it into the field. To create the connection, you will need to shorten it manually. Check the section about the limitation of a full connection string on how to accomplish it.

   On-premises data gateway (personal mode) does not have this limitation.

8. Proceed by choosing the newly created connection:

   ODBC{"connectionstring":"dsn=GoogleBigqueryDSN"}

   

9. Finally, you are at the final step where you can refresh the semantic model:

   

Refresh using On-premises data gateway (personal mode)

Here are the instructions on how to refresh a Power BI semantic model using On-premises data gateway (personal mode):

1. Go to Power BI My workspace, hover your mouse cursor on your semantic model and click Settings:

   

2. If you see this view, it means you have to install On-premises data gateway (personal mode):

   

3. Install On-premises data gateway (personal mode) and sign-in:

   
   Use the same email address you use when logging in into Power BI account.

4. Again, go to your semantic model Settings, expand Data source credentials, click Edit credentials, select Authentication method together with Privacy level, and then click Sign in button:

   dsn=GoogleBigqueryDSN

   

5. Finally, you are ready to refresh your semantic model:

   

Actions supported by Google BigQuery Connector

Learn how to perform common Google BigQuery actions directly in Power BI with these how-to guides:

• [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
• Generic Request
• Generic Request (Bulk Write)

Conclusion

In this article we showed you how to connect to Google BigQuery in Power BI and integrate data without any coding, saving you time and effort. It's worth noting that ZappySys API Driver allows you to connect not only to Google BigQuery, but to any Java application that supports JDBC (just use a different JDBC driver and configure it appropriately).

We encourage you to download Google BigQuery Connector for Power BI and see how easy it is to use it for yourself or your team.

If you have any questions, feel free to contact ZappySys support team. You can also open a live chat immediately by clicking on the chat icon below.

Download Google BigQuery Connector for Power BI Documentation

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