Google BigQuery Connector for Power BI In this article you will learn how to integrate Google BigQuery data in Power BI without coding in just a few clicks (live / bi-directional connection to Google BigQuery). Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector. Using Google BigQuery Connector you will be able to connect, read, and write data from within Power BI. Follow the steps below to see how we would accomplish that. Download Documentation The driver mentioned above is part of ODBC PowerPack which is a collection of high-performance Drivers for various API data source (i.e. REST API, JSON, XML, CSV, Amazon S3 and many more). Using familiar SQL query language you can make live connections and read/write data from API sources or JSON / XML / CSV Files inside SQL Server (T-SQL) or your favorite Reporting (i.e. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i.e. Informatica, Talend, Pentaho, SSIS). You can also call our drivers from programming languages such as JAVA, C#, Python, PowerShell etc. If you are new to ODBC and ZappySys ODBC PowerPack then check the following links to get started. ODBC PowerPack - System Requirements and Installation Getting Started - Using ODBC PowerPack ODBC API Driver Help File

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

• How to download / install required driver for Google BigQuery integration in Power BI
• How to configure connection for Google BigQuery
• Features about API Driver (Authentication / Query Language / Examples / Driver UI)
• Using Google BigQuery Connection 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. Install ZappySys ODBC PowerPack.

2. Open ODBC Data Sources (x64):
   

3. Create a User Data Source (User DSN) based on ZappySys API Driver

   ZappySys API Driver

   
   You should create a System DSN (instead of a User DSN) if the client application is launched under a Windows System Account, e.g. as a Windows Service. If the client application is 32-bit (x86) running with a System DSN, use ODBC Data Sources (32-bit) instead of the 64-bit version.

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]

   Steps how to get and use Google BigQuery credentials
   This connection can be configured using two ways. Use Default App (Created by ZappySys) OR Use Custom App created by you.
   To use minimum settings you can start with ZappySys created App. Just change UseCustomApp=false on the properties grid so you dont need ClientID / Secret. When you click Generate Token you might see warning about App is not trusted (Simply Click Advanced Link to expand hidden section and then click Go to App link to Proceed).

   To register custom App, perform the following steps (Detailed steps found in the help link at the end)

   1. Go to Google API Console
   2. From the Project Dropdown (usually found at the top bar) click Select Project
   3. On Project Propup click CREATE PROJECT
   4. Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
   5. Click ENABLE APIS AND SERVICES
   6. Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
   7. Search BigQuery API. Select and click ENABLE
   8. Search Cloud Resource Manager API. Select and click ENABLE
   9. Go to back to main screen of Google API Console

   10. Click OAuth consent screen Tab. Enter necessary details and Save.

       1. Choose Testing as Publishing status
       2. Set application User type to Internal, if possible
       3. If MAKE INTERNAL option is disabled, then add a user in Test users section, which you will use in authentication process when generating Access and Refresh tokens
   11. Click Credentials Tab
   12. Click CREATE CREDENTIALS (some where in topbar) and select OAuth Client ID option.
   13. When prompted Select Application Type as Desktop App and click Create to receive your ClientID and Secret. Later on you can use this information now to configure Connection with UseCustomApp=true.
   14. Go to OAuth Consent Screen tab. Under Publishing Status click PUBLISH APP to ensure your refresh token doesnt expire often. If you planning to use App for Private use then do not have to worry about Verification Status after Publish.

   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 (Using Private Key File) [OAuth]

   Steps how to get and use Google BigQuery credentials
   Use these steps to authenticate as service account rather than Google / GSuite User. Learn more about service account here

   Basically to call Google API as Service account we need to perform following steps listed in 3 sections (Detailed steps found in the help link at the end)

   Create Project

   First thing is create a Project so we can call Google API. Skip this section if you already have Project (Go to next section)

   1. Go to Google API Console
   2. From the Project Dropdown (usually found at the top bar) click Select Project
   3. On Project Propup click CREATE PROJECT
   4. Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
   5. Click ENABLE APIS AND SERVICES
   6. Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
   7. Search BigQuery API. Select and click ENABLE
   8. Search Cloud Resource Manager API. Select and click ENABLE

   Create Service Account

   Once Project is created and APIs are enabled we can now create a service account under that project. Service account has its ID which looks like some email ID (not to confuse with Google /Gmail email ID)

   1. Go to Create Service Account
   2. From the Project Dropdown (usually found at the top bar) click Select Project
   3. Enter Service account name and Service account description
   4. Click on Create. Now you should see an option to assign Service Account permissions (See Next Section).

   Give Permission to Service Account

   By default service account cant access BigQuery data or List BigQuery Projects so we need to give that permission using below steps.

   1. After you Create Service Account look for Permission drop down in the Wizard.
   2. Choose BigQuery -> BigQuery Admin role so we can read/write data. (NOTE: If you just need read only access then you can choose BigQuery Data Viewer)
   3. Now choose one more Project -> Viewer and add that role so we can query Project Ids.
   4. Click on Continue. Now you should see an option to Create Key (See Next Section).

   Create Key (P12)

   Once service account is created and Permission is assigned we need to create key file.

   1. In the Cloud Console, click the email address for the service account that you created.
   2. Click Keys.
   3. Click Add key, then click Create new key.
   4. Click Create and select P12 format. A P12 key file is downloaded to your computer. We will use this file in our API connection.
   5. Click Close.
   6. Now you may use downloaded *.p12 key file as secret file and Service Account Email as Client ID (e.g. some_name@some_name.iam.gserviceaccount.com).

   Manage Permissions / Give Access to Other Projects

   We saw how to add permissions for Service Account during Account Creation Wizard but if you ever wish to edit after its created or you wish to give permission for other projects then perform forllowing steps.

   1. From the top Select Project for which you like to edit Permission.
   2. Go to IAM Menu option (here)
      Link to IAM: https://console.cloud.google.com/iam-admin/iam
   3. Goto Permissions tab. Over there you will find ADD button.
   4. Enter Service account email for which you like to grant permission. Select role you wish to assign.

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

   GoogleBigqueryDSN

   Google BigQuery

   Service Account (Using Private Key File) [OAuth]

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

   Required Parameters
   Service Account Email	Fill-in the parameter...
   P12 Service Account Private Key Path (i.e. *.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

   

6. Once the data source has been configured, you can preview data. Select the Preview tab and use settings similar to the following to preview data:
   

7. Click OK to finish creating the data source.

Video instructions

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

                SELECT
                    ProductID,
                    ProductName,
                    SupplierID,
                    CategoryID,
                    QuantityPerUnit,
                    UnitPrice
                FROM Products
                WHERE UnitPrice > 20
            

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 - 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 dont want to import but link it. Power BI Offers very useful feature for this scenario. Its called DirectQuery Option. In this section we will explore how to use DirectQuery along with ZappySys Drivers.

Out of the box ZappySys Drivers wont 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 and then we will issue OPENROWSET queries from Power BI to SQL Server and it will then call Google BigQuery via ZappySys Data Gateway.

1. First read this article carefully, How to query Google BigQuery API in SQL Server.
2. Once linked server is configured we are ready to issue API 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 name below).

   SELECT * FROM OPENQUERY([GOOGLE_BIGQUERY_LINKED_SERVER], 'SELECT * FROM Customers')

   SELECT * FROM OPENQUERY([GOOGLE_BIGQUERY_LINKED_SERVER], 'SELECT * FROM Customers')

   

   
   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:

   

Advanced topics

Create Custom Stored Procedure in ZappySys Driver

You can create procedures to encapsulate custom logic and then only pass handful parameters rather than long SQL to execute your API call.

Steps to create Custom Stored Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here

1. Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
   

2. Enter the desired Procedure name and click on OK:
   

3. Select the created Stored Procedure and write the your desired stored procedure and Save it and it will create the custom stored procedure in the ZappySys Driver:
   Here is an example stored procedure for ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
   

   CREATE PROCEDURE [usp_get_orders]
       @fromdate = '<<yyyy-MM-dd,FUN_TODAY>>'
    AS
       SELECT * FROM Orders where OrderDate >= '<@fromdate>';
   

   
   

4. That's it now go to Preview Tab and Execute your Stored Procedure using Exec Command. In this example it will extract the orders from the date 1996-01-01:
   

   Exec usp_get_orders '1996-01-01';

   
   

5. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([MY_API_SERVICE], 'EXEC usp_get_orders @fromdate=''1996-07-30''')

   
   

6. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Create Custom Virtual Table in ZappySys Driver

ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.

If you're dealing with Microsoft Access and need to import data from an SQL query, it's important to note that Access doesn't allow direct import of SQL queries. Instead, you can create custom objects (Virtual Tables) to handle the import process.

Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.

1. Go to Custom Objects Tab and Click on Add button and Select Add Table:
   

2. Enter the desired Table name and click on OK:
   

3. And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.

4. Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
   Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
   

   SELECT
     "ShipCountry",
     "OrderID",
     "CustomerID",
     "EmployeeID",
     "OrderDate",
     "RequiredDate",
     "ShippedDate",
     "ShipVia",
     "Freight",
     "ShipName",
     "ShipAddress",
     "ShipCity",
     "ShipRegion",
     "ShipPostalCode"
   FROM "Orders"
   Where "ShipCountry"='USA'

   
   

5. That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
   

   SELECT * FROM "vt__usa_orders_only"

   
   

6. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([MY_API_SERVICE], 'EXEC [usp_get_orders] ''1996-01-01''')

   
   

7. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Actions supported by Google BigQuery Connector

Google BigQuery Connector Examples for Power BI Connection

This page offers a collection of SQL examples designed for seamless integration with the ZappySys API ODBC Driver under ODBC Data Source (36/64) or ZappySys Data Gateway, enhancing your ability to connect and interact with Prebuilt Connectors effectively.

#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) */

#DirectSQL 
SELECT name, gender, SUM(number) AS total
FROM bigquery-public-data.usa_names.usa_1910_2013
GROUP BY name, gender
ORDER BY total DESC
LIMIT 10

#DirectSQL DELETE FROM TestDataset.MyTable Where Id > 5

#DirectSQL 
SELECT id, col_timestamp, CAST(col_timestamp as DATE) AS timestamp_as_date, CAST(col_timestamp as DATETIME) AS timestamp_as_datetime
FROM MyProject.MyDataset.MyTable
LIMIT 10

#DirectSQL CREATE TABLE TestDataset.Table1 (ID INT64,Name STRING,BirthDate DATETIME, Active BOOL)

#DirectSQL 
#DirectSQL 
Update TestDataset.DataTypeTest 
Set ColTime='23:59:59.123456',
 ColGeography=ST_GEOGPOINT(34.150480, -84.233870),
 ColRecord=(1,"AA","Column3 data"),
 ColBigNumeric=1222222222222222222.123456789123456789123456789123456789,
 ColJson= JSON_ARRAY('{"doc":1, "values":[{"id":1},{"id":2}]}') 
Where ColInteger=1

#DirectSQL DROP TABLE IF EXISTS Myproject.Mydataset.Mytable

#DirectSQL CALL TestDataset.usp_GetData(1)

INSERT INTO MyBQTable1(SomeBQCol1, SomeBQCol2) Values(1,'AAA')
--WITH(DatasetId='TestDataset',Output='*')
--WITH(DatasetId='TestDataset',ProjectId='MyProjectId',Output='*')

INSERT INTO MyBQTable1 
SOURCE(
    'MSSQL'
  , 'Data Source=localhost;Initial Catalog=tempdb;Initial Catalog=tempdb;Integrated Security=true'
  , 'SELECT Col1 as SomeBQCol1,Col2 as SomeBQCol2 FROM SomeTable Where SomeCol=123'
)
--WITH(DatasetId='TestDataset',Output='*')
--WITH(DatasetId='TestDataset',ProjectId='MyProjectId',Output='*')

INSERT INTO MyBQTable1 
SOURCE(
    'ODBC'
  , 'DSN=MyDsn'
  , 'SELECT Col1 as SomeBQCol1,Col2 as SomeBQCol2 FROM SomeTable Where SomeCol=123'
) 
WITH(DatasetId='TestDataset')

INSERT INTO MyBQTable1 
SOURCE(
    'ODBC'
  , 'Driver={ZappySys JSON Driver};Src='https://some-url/get-data''
  , 'SELECT Col1 as SomeBQCol1,Col2 as SomeBQCol2 FROM _root_'
)
--WITH(DatasetId='TestDataset',Output='*')
--WITH(DatasetId='TestDataset',ProjectId='MyProjectId',Output='*')

SELECT * FROM list_projects

SELECT * FROM list_datasets
--WITH(ProjectId='MyProjectId')

SELECT * FROM list_tables
--WITH(ProjectId='MyProjectId')
--WITH(ProjectId='MyProjectId',DatasetId='MyDatasetId')

SELECT * FROM delete_dataset WITH(DatasetId='MyDatasetId', deleteContents='False')

Conclusion

In this article we discussed how to connect to Google BigQuery in Power BI and integrate data without any coding. Click here to Download Google BigQuery Connector for Power BI and try yourself see how easy it is. If you still have any question(s) then ask here or simply click on live chat icon below and ask our expert (see bottom-right corner of this page).

Download Google BigQuery Connector for Power BI Documentation 

More integrations

Other application integration scenarios for Google BigQuery

SSIS

SQL Server

SSRS

Informatica

MS Access

MS Excel

SSAS

C#

Python

JAVA

Tableau

SAP Crystal Reports

Azure Data Factory (Pipeline)

Azure Data Factory (SSIS)

Talend Studio

UiPath

PowerShell

ODBC

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Mailchimp

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Smartsheet

Stripe

YouTube

Zendesk

Zoho CRM

Zoho SalesIQ

Zoom

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JSON

JSON File

XML

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JDBC-ODBC Bridge

Salesforce

CSV

Amazon S3 JSON File

Amazon S3 XML File

Amazon S3 CSV File

Azure Blob JSON File

Azure Blob XML File

Azure Blob CSV File

FTP/SFTP JSON File

FTP/SFTP XML File

FTP/SFTP CSV File

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