Google BigQuery Connector for Tableau In this article you will learn how to integrate Google BigQuery data in Tableau 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 Tableau. 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 Power BI 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 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

Create Data Source in ZappySys Data Gateway based on API Driver

1. Download and install ZappySys ODBC PowerPack.

2. Search for gateway in start menu and Open ZappySys Data Gateway:
   

3. Go to Users Tab to add our first Gateway user. Click Add; we will give it a name tdsuser and enter password you like to give. Check Admin option and click OK to save. We will use these details later when we create linked server:
   

4. Now we are ready to add a data source. Click Add, give data source a name (Copy this name somewhere, we will need it later) and then select Native - ZappySys API Driver. Finally, click OK. And it will create the Data Set for it and open the ZS driver UI.

   GoogleBigQueryDSN

   
   

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 [OAuth]

   Steps to get 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	Fill in the parameter...
   ClientSecret	Fill in the parameter...
   Scope	Fill in the parameter...
   RetryMode	Fill in the parameter...
   RetryStatusCodeList	Fill in the parameter...
   RetryCountMax	Fill in the parameter...
   RetryMultiplyWaitTime	Fill in the parameter...
   Job Location	Fill in the parameter...
   Redirect URL (Only for Web App)	Fill in the parameter...

   

    Service Account (Using Private Key File) [OAuth]

   Steps to get 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	Fill in the parameter...
   RetryMode	Fill in the parameter...
   RetryStatusCodeList	Fill in the parameter...
   RetryCountMax	Fill in the parameter...
   RetryMultiplyWaitTime	Fill in the parameter...
   Job Location	Fill in the parameter...

   

7. 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:
   

8. Click OK to finish creating the data source.

Read data in SQL Server from the ZappySys Data Gateway

1. To read the data in SQL Server the first thing you have to do is create a Linked Server. Go to SQL Server Management Studio and configure it in a similar way:
   

2. Then click on Security option and configure username we created in ZappySys Data Gateway in one of the previous steps:
   

3. Optional: Under the Server Options, Enable RPC and RPC Out and Disable Promotion of Distributed Transactions(MSDTC).

   

   ---

   You need to enable RPC Out if you plan to use EXEC(...) AT [MY_LINKED_SERVER_NAME] rather than OPENQUERY.
   If don't enabled it, you will encounter the Server 'MY_LINKED_SERVER_NAME' is not configured for RPC error.

   Query Example:

   EXEC('Select * from Products') AT [MY_LINKED_SERVER_NAME]

   ---

   If you plan to use 'INSERT INTO...EXEC(....) AT [MY_LINKED_SERVER_NAME]' in that case you need to Disable Promotion of Distributed Transactions(MSDTC).
   If don't disabled it, you will encounter the The operation could not be performed because OLE DB provider "SQLNCLI11" for linked server "MY_LINKED_SERVER_NAME" was unable to begin a distributed transaction. error.

   Query Example:

   Insert Into dbo.Products
    EXEC('Select * from Products') AT [MY_LINKED_SERVER_NAME]
   

   ---

4. Finally, open a new query and execute a query we saved in one of the previous steps:
   

   SELECT * FROM OPENQUERY([MY_LINKED_SERVER_NAME], 'SELECT * FROM Products');

   

Create Linked Server using Code

    USE [master]
    GO
    --///////////////////////////////////////////////////////////////////////////////////////
    --Run below code in SSMS to create Linked Server and use ZappySys Drivers in SQL Server
    --///////////////////////////////////////////////////////////////////////////////////////

    //Replace YOUR_GATEWAY_USER, YOUR_GATEWAY_PASSWORD
    //Replace localhost with IP/Machine name if ZappySys Gateway Running on different machine other than SQL Server
    //Replace Port 5000 if you configured gateway on a different port


    --1. Configure your gateway service as per this article https://zappysys.com/links?id=10036

    --2. Make sure you have SQL Server Installed. You can download FREE SQL Server Express Edition from here if you dont want to buy Paid version https://www.microsoft.com/en-us/sql-server/sql-server-editions-express

    --Uncomment below if you like to drop linked server if it already exists
    --EXEC master.dbo.sp_dropserver @server=N'LS_GoogleBigQueryDSN', @droplogins='droplogins'

    --3. Create new linked server

    EXEC master.dbo.sp_addlinkedserver
      @server = N'LS_GoogleBigQueryDSN'  --Linked server name (this will be used in OPENQUERY sql
    , @srvproduct=N''
    ---- For MSSQL 2012,2014,2016 and 2019 use below (SQL Server Native Client 11.0)---
    , @provider=N'SQLNCLI11'
    ---- For MSSQL 2022 or higher use below (Microsoft OLE DB Driver for SQL Server)---
    --, @provider=N'MSOLEDBSQL'
    , @datasrc=N'localhost,5000' --//Machine / Port where Gateway service is running
    , @provstr=N'Network Library=DBMSSOCN;'
    , @catalog=N'GoogleBigQueryDSN' --Data source name you gave on Gateway service settings

    --4. Attach gateway login with linked server

    EXEC master.dbo.sp_addlinkedsrvlogin
    @rmtsrvname=N'LS_GoogleBigQueryDSN'  --linked server name
    , @useself=N'False'
    , @locallogin=NULL
    , @rmtuser=N'YOUR_GATEWAY_USER' --enter your Gateway user name
    , @rmtpassword='YOUR_GATEWAY_PASSWORD'  --enter your Gateway user's password
    GO

    --5. Enable RPC OUT (This is Optional - Only needed if you plan to use EXEC(...) AT YourLinkedServerName rather than OPENQUERY
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'rpc', true;
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'rpc out', true;

    --Disable MSDTC - Below needed to support INSERT INTO from EXEC AT statement
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'remote proc transaction promotion', false;

    --Increase query timeout if query is going to take longer than 10 mins (Default timeout is 600 seconds)
    --EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'query timeout', 1200;
    GO

Create View in SQL Server

Finally, use this or similar query in a view or stored procedure, which you will be able to use in Tableau. We will create a view to return invoices:

1. CREATE VIEW vwApiInvoices AS
    SELECT * FROM OPENQUERY([MY_LINKED_SERVER_NAME], 'SELECT * FROM Invoices')

Read data in Tableau from SQL Server

Actually, we will be getting data from SQL Server which in turn will be getting data from ZappySys Data Gateway data source. Let's begin and see how to accomplish that:

1. Open Tableau Desktop and click File > New
2. To create new Connection click More > Microsoft SQL Server > Enter your credentials to connect to SQL Server (in our example before we used tdsuser):
3. Once connection is created for SQL Server we can read Google BigQuery data 3 different ways:
   1. Query View which contains OPENQUERY to Linked Server for Google BigQuery data
   2. Use direct SQL Query using OPENQUERY
   3. Use Stored Procedure (Mostly useful to parameterize calls
4. See below example to pull data from Google BigQuery in Tableau using SQL View approach:
5. Once your data sources are created you can click on Sheet1 and drag fields to create visualizations for Tableau Dashboard:

Passing Parameters to Google BigQuery calls in Tableau (Dynamic SQL)

1. First lets create a stored procedure in SQL Server for Parameter Example. Notice how we added WITH RESULT SETS in the code to describe metadata.

   --DROP PROC dbo.usp_GetInvoicesByCountry
   --GO
   /*
   Purpose: Parameterize Google BigQuery call via SQL. Call ZappySys Drivers inside SQL Server.
   */
   CREATE PROC dbo.usp_GetInvoicesByCountry
   	@country varchar(100) 
   AS 
   
   DECLARE @sql varchar(max)
   --//Escape single ticks carefully
   SET @sql =  'SELECT OrderID,CustomerID,Country,Quantity FROM $
   WITH (Src=''https://services.odata.org/V3/Northwind/Northwind.svc/Invoices?$format=json@filter=Country eq '+ @country +'''
   	 ,Filter=''$.value[*]''
   	 ,DataFormat=''OData''
   )'
   
   DECLARE @sqlFull varchar(max)
   SET @sqlFull='SELECT * FROM OPENQUERY( [LS] , ''' + REPLACE( @sql, '''', '''''' ) + ''' )'
   PRINT @sqlFull --//For DEBUG purpose
   
   EXECUTE (@sqlFull) 
   WITH RESULT SETS ( 
    (OrderID int,CustomerID varchar(100),Country varchar(100),Quantity int) --//describe first result. If you don't do this then wont work in Tableau 
   )
   GO
   -- Example call
   EXEC dbo.usp_GetInvoicesByCountry @country='Germany'

2. Once you create a stored procedure go to Tableau datasource and select Database which contains the stored procedure we just created.
3. Now find your stored proc and drag it on the datasource pane. You will see parameters UI as below. You can create new parameter - Select New Parameter under Value Column.
4. Thats it now you can reuse your parameterized datasource anywhere in Dashboard.
5. If you have need to select Parameters from predefined values rather than free text then edit your parameter and select List option. Define values you like to select from as below.
6. When you create Tableau Dashboard you will see Parameter dropdown (If you selected List) elase you may see Textbox to enter custom value.

Firewall settings

So far we have assumed that Gateway is running on the same machine as SQL Server. However there will be a case when ZappySys ODBC PowerPack is installed on a different machine than SQL Server. In such case you may have to perform additional Firewall configurations. On most computers firewall settings wont allow outside traffic to ZappySys Data Gateway. In such case perform following steps to allow other machines to connect to Gateway.

If you are using newer version of ZappySys Data Gateway then adding firewall rule is just a single click.

1. Search for gateway in start menu and open ZappySys Data Gateway.
2. Go to Firewall Tab and click Add Firewall Rule button like below. This will create Firewall rule to all Inbound Traffic on Port 5000 (Unless you changed it).

1. Search for Windows Firewall Advanced Security in start menu.
2. Under Inbound Rules > Right click and click [New Rule] >> Click Next
3. Select Port on Rule Type >> Click Next
4. Click on TCP and enter port number under specified local port as 5000 (use different one if you changed Default port) >> Click Next
5. Select Profile (i.e. Private, Public) >> Click Next
6. Enter Rule name [i.e. ZappySys Data Gateway – Allow Inbound ] >> Click Next
7. Click OK to save the rule

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 Tableau 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 Tableau and integrate data without any coding. Click here to Download Google BigQuery Connector for Tableau 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 Tableau Documentation 

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