Google BigQuery Connector for Tableau 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 Tableau without coding. We will use high-performance Google BigQuery Connector to easily connect to Google BigQuery and then access the data inside Tableau. Let's follow the steps below to see how we can accomplish that! Download Documentation Google BigQuery Connector for Tableau 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.

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Create Data Source in ZappySys Data Gateway based on API Driver

1. Download and install 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]

   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)

   

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

   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 Tableau 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.

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 Tableau:

   

   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).

10. Click OK to finish creating the data source.

Read data in SQL Server using ZappySys Data Gateway

To read the data in SQL Server, the first thing you have to do is create a Linked Server:

1. First, let's open SQL Server Management Studio, create a new Linked Server, and start configuring it:

   LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY

   Microsoft OLE DB Driver for SQL Server

   localhost,5000

   GoogleBigqueryDSN

   GoogleBigqueryDSN

   
   Choose SQL Server Native Client 11.0 as Provider if you don't see the option shown above.

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

   

3. Optional step. 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 [LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY] rather than OPENQUERY.
   If don't enabled it, you will encounter the Server 'LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY' is not configured for RPC error.

   Query Example:

   EXEC('#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) */') AT [LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY]

   ---

   If you plan to use 'INSERT INTO <TABLE> EXEC(...) AT [LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY]' 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('#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) */') AT [LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY]
   

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

   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) */')

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_TO_GOOGLE_BIGQUERY_IN_GATEWAY', @droplogins='droplogins'

--3. Create new linked server

EXEC master.dbo.sp_addlinkedserver
    @server = N'LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY'  --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_TO_GOOGLE_BIGQUERY_IN_GATEWAY'  --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_TO_GOOGLE_BIGQUERY_IN_GATEWAY', 'rpc', true;
EXEC sp_serveroption 'LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY', 'rpc out', true;

--Disable MSDTC - Below needed to support INSERT INTO from EXEC AT statement
EXEC sp_serveroption 'LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY', '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_TO_GOOGLE_BIGQUERY_IN_GATEWAY', '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([LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY], '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_TO_GOOGLE_BIGQUERY_IN_GATEWAY], ''' + 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([LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY], '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([LS_TO_GOOGLE_BIGQUERY_IN_GATEWAY], '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 
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 showed you how to connect to Google BigQuery in Tableau 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 Tableau 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 Tableau Documentation

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