Tableau Google BigQuery Connector

In this article you will learn how to integrate Google BigQuery data to 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 Help File Buy

Driver mentioned in this article 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.

Create Data Source in ZappySys Data Gateway based on ZappySys API Driver

Download and install ZappySys ODBC PowerPack.
Search for gateway in start menu and Open ZappySys Data Gateway:
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:
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
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

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)

Go to Google API Console
From the Project Dropdown (usually found at the top bar) click Select Project
On Project Propup click CREATE PROJECT
Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
Click ENABLE APIS AND SERVICES
Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
Search BigQuery API. Select and click ENABLE
Search Cloud Resource Manager API. Select and click ENABLE
Go to back to main screen of Google API Console
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
Click Credentials Tab
Click CREATE CREDENTIALS (some where in topbar) and select OAuth Client ID option.
When prompted Select Application Type as Desktop App and click Create to receive your ClientID and Secret. You can use this information now to configure Connection with UseCustomApp=true.

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	Fill in the parameter...
DatasetId	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...

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)

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

Go to Create Service Account
From the Project Dropdown (usually found at the top bar) click Select Project
Enter Service account name and Service account description
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.

After you Create Service Account look for Permission drop down in the Wizard.
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)
Now choose one more Project -> Viewer and add that role so we can query Project Ids.
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.

In the Cloud Console, click the email address for the service account that you created.
Click Keys.
Click Add key, then click Create new key.
Click Create and select P12 format. A P12 key file is downloaded to your computer. We will use this file in our API connection.
Click Close.
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.

From the top Select Project for which you like to edit Permission.
Go to IAM Menu option (here)
Link to IAM: https://console.cloud.google.com/iam-admin/iam
Goto Permissions tab. Over there you will find ADD button.
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...
Service Account Private Key Path (i.e. *.p12)	Fill in the parameter...
ProjectId	Fill in the parameter...
DatasetId	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...

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:
Click OK to finish creating the data source.

Read data in SQL Server from the ZappySys Data Gateway data source

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:
Then click on Security option and configure username we created in ZappySys Data Gateway in one of the previous steps:
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');
```
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:
```
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:

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

Passing Parameters to REST API calls in Tableau (Dynamic SQL)

Now let's look at scenario where you have to pass parameters to build Dynamic Dashboard. You can try to insert Parameters in your Direct SQL when you build Dynamic SQL but we found some issues with that so we are going to suggest Stored Procedure approach. For more information on Known issue on Dynamic Metadata Check this post.

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 REST API 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'

Once you create a stored procedure go to Tableau datasource and select Database which contains the stored procedure we just created.
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.
Thats it now you can reuse your parameterized datasource anywhere in Dashboard.
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.
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.

Method-1 (Preferred)

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

Search for gateway in start menu and open ZappySys Data Gateway.
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).

Method-2 Here is another way to add / edit Inbound Traffic rule in windows firewall. Use below method if you choose to customize your rule (for advanced users).

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

SQL Server Firewall Allow Inbound Data Gateway

Create Custom Store 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 Store Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here

Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
Enter the desired Procedure name and click on OK:
Select the created Store Procedure and write the your desired store procedure and Save it and it will create the custom store 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>';
```
That's it now go to Preview Tab and Execute your Store 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';
```
Let's generate the SQL Server Query Code to make the API call using store 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''')
```
Now go to SQL served and execute that query and it will make the API call using store 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.

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.

Go to Custom Objects Tab and Click on Add button and Select Add Table:
Enter the desired Table name and click on OK:
And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.
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'
```
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"
```
Let's generate the SQL Server Query Code to make the API call using store 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''')
```
Now go to SQL served and execute that query and it will make the API call using store procedure and provide you the response.

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

Actions supported by Google BigQuery Connector

Google BigQuery Connector support following actions for REST API integration. If some actions are not listed below then you can easily edit Connector file and enhance out of the box functionality.

Read Data using SQL Query -OR- Execute Script (i.e. CREATE, SELECT, INSERT, UPDATE, DELETE)

Runs a BigQuery SQL query synchronously and returns query results if the query completes within a specified timeout

Parameter

Description

SQL Statement (i.e. SELECT / DROP / CREATE)

Option	Value
Example1	SELECT title,id,language,wp_namespace,reversion_id ,comment,num_characters FROM bigquery-public-data.samples.wikipedia LIMIT 1000
Example2	CREATE TABLE TestDataset.Table1 (ID INT64,Name STRING,BirthDate DATETIME, Active BOOL)
Example3	INSERT TestDataset.Table1 (ID, Name,BirthDate,Active) VALUES(1,'AA','2020-01-01',true),(2,'BB','2020-01-02',true),(3,'CC','2020-01-03',false)

Use Legacy SQL Syntax?

Option	Value
false	false
true	true

timeout (Milliseconds)

Wait until timeout is reached.

Option	Value
false	false
true	true

Job Location

The geographic location where the job should run. For Non-EU and Non-US datacenters we suggest you to supply this parameter to avoid any error.

Option	Value
System Default
Data centers in the United States	US
Data centers in the European Union	EU
Columbus, Ohio	us-east5
Iowa	us-central1
Las Vegas	us-west4
Los Angeles	us-west2
Montréal	northamerica-northeast1
Northern Virginia	us-east4
Oregon	us-west1
Salt Lake City	us-west3
São Paulo	southamerica-east1
Santiago	southamerica-west1
South Carolina	us-east1
Toronto	northamerica-northeast2
Delhi	asia-south2
Hong Kong	asia-east2
Jakarta	asia-southeast2
Melbourne	australia-southeast2
Mumbai	asia-south1
Osaka	asia-northeast2
Seoul	asia-northeast3
Singapore	asia-southeast1
Sydney	australia-southeast1
Taiwan	asia-east1
Tokyo	asia-northeast1
Belgium	europe-west1
Finland	europe-north1
Frankfurt	europe-west3
London	europe-west2
Madrid	europe-southwest1
Milan	europe-west8
Netherlands	europe-west4
Paris	europe-west9
Warsaw	europe-central2
Zürich	europe-west6
AWS - US East (N. Virginia)	aws-us-east-1
Azure - East US 2	azure-eastus2
Custom Name (Type your own)	type-region-id-here

Read Table Rows

Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.

Parameter	Description
ProjectId	Leave this value blank to use ProjectId from connection settings
DatasetId	Leave this value blank to use DatasetId from connection settings
TableId

[$parent.tableReference.datasetId$].[$parent.tableReference.tableId$]

Read data from [$parent.tableReference.datasetId$].[$parent.tableReference.tableId$] for project .

Parameter	Description

List Projects

Lists Projects that the caller has permission on and satisfy the specified filter.

Parameter	Description
SearchFilter	An expression for filtering the results of the request. Filter rules are case insensitive. If multiple fields are included in a filter query, the query will return results that match any of the fields. Some eligible fields for filtering are: name, id, labels.{key} (where key is the name of a label), parent.type, parent.id, lifecycleState. Example: name:how*

List Datasets

Lists all BigQuery datasets in the specified project to which the user has been granted the READER dataset role.

Parameter

Description

ProjectId

SearchFilter

An expression for filtering the results of the request. Filter rules are case insensitive. If multiple fields are included in a filter query, the query will return results that match any of the fields. Some eligible fields for filtering are: name, id, labels.{key} (where key is the name of a label), parent.type, parent.id, lifecycleState. Example: name:how*

all

Whether to list all datasets, including hidden ones

Option	Value
True	True
False	False

Create Dataset

Creates a new empty dataset.

Parameter	Description
ProjectId
Dataset Name	Enter dataset name
Description

Delete Dataset

Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name.

Parameter

Description

ProjectId

DatasetId

Delete All Tables

If True, delete all the tables in the dataset. If False and the dataset contains tables, the request will fail. Default is False

Option	Value
True	True
False	False

Delete Table

Parameter	Description
ProjectId
DatasetId
TableId

List Tables

Lists BigQuery Tables for the specified project / dataset to which the user has been granted the READER dataset role.

Parameter	Description
ProjectId
DatasetId

Get Query Schema (From SQL)

Runs a BigQuery SQL query synchronously and returns query schema

Parameter

Description

SQL Query

Use Legacy SQL Syntax?

Option	Value
false	false
true	true

timeout (Milliseconds)

Wait until timeout is reached.

Option	Value
false	false
true	true

Job Location

The geographic location where the job should run. For Non-EU and Non-US datacenters we suggest you to supply this parameter to avoid any error.

Option	Value
System Default
Data centers in the United States	US
Data centers in the European Union	EU
Columbus, Ohio	us-east5
Iowa	us-central1
Las Vegas	us-west4
Los Angeles	us-west2
Montréal	northamerica-northeast1
Northern Virginia	us-east4
Oregon	us-west1
Salt Lake City	us-west3
São Paulo	southamerica-east1
Santiago	southamerica-west1
South Carolina	us-east1
Toronto	northamerica-northeast2
Delhi	asia-south2
Hong Kong	asia-east2
Jakarta	asia-southeast2
Melbourne	australia-southeast2
Mumbai	asia-south1
Osaka	asia-northeast2
Seoul	asia-northeast3
Singapore	asia-southeast1
Sydney	australia-southeast1
Taiwan	asia-east1
Tokyo	asia-northeast1
Belgium	europe-west1
Finland	europe-north1
Frankfurt	europe-west3
London	europe-west2
Madrid	europe-southwest1
Milan	europe-west8
Netherlands	europe-west4
Paris	europe-west9
Warsaw	europe-central2
Zürich	europe-west6
AWS - US East (N. Virginia)	aws-us-east-1
Azure - East US 2	azure-eastus2
Custom Name (Type your own)	type-region-id-here

Get Table Schema

Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.

Parameter	Description
DatasetId
TableId

insert_table_data

Parameter	Description
ProjectId
DatasetId
TableId

post_[$parent.tableReference.datasetId$]_[$parent.tableReference.tableId$]

Generic Request

This is generic endpoint. Use this endpoint when some actions are not implemented by connector. Just enter partial URL (Required), Body, Method, Header etc. Most parameters are optional except URL.

Parameter	Description
Url	API URL goes here. You can enter full URL or Partial URL relative to Base URL. If it is full URL then domain name must be part of ServiceURL or part of TrustedDomains
Body	Request Body content goes here
IsMultiPart	Check this option if you want to upload file(s) (i.e. POST RAW file data) or send data using Multi-Part encoding method (i.e. Content-Type: multipart/form-data). Multi-Part request allows you to mix key/value and upload files in same request. On the other hand raw upload allows only single file upload (without any key/value) ==== Raw Upload (Content-Type: application/octet-stream) ===== To upload single file in raw mode check this option and specify full file path starting with @ sign in the Body (e.g. @c:\data\myfile.zip ) ==== Form-Data / Multipart Upload (Content-Type: multipart/form-data) ===== To treat your Request data as multi part fields you must specify key/value pairs separated by new lines into RequestData field (i.e. Body). Each key value pair is entered on new-line and key/value are separated using equal sign (=). Preceding and trailing spaces are ignored also blank lines are ignored. If field value has some any special character(s) then use escape sequence (e.g. For NewLine: \r\n, For Tab: \t, For at (@): \@). When value of any field starts with at sign (@) its automatically treated as File you want to upload. By default file content type is determined based on extension however you can supply content type manually for any field using this way [ YourFileFieldName.Content-Type=some-content-type ]. By default File Upload Field always includes Content-Type in the request (non file fields do not have content-type by default unless you supply manually). For some reason if you dont want to use Content-Type header in your request then supply blank Content-Type to exclude this header altogather [e.g. SomeFieldName.Content-Type= ]. In below example we have supplied Content-Type for file2 and SomeField1, all other fields are using default content-type. See below Example of uploading multiple files along with additional fields. If some API requires you to pass Content-Type: multipart/form-data rather than multipart/form-data then manually set Request Header => Content-Type: multipart/mixed (it must starts with multipart/ else will be ignored). file1=@c:\data\Myfile1.txt file2=@c:\data\Myfile2.json file2.Content-Type=application/json SomeField1=aaaaaaa SomeField1.Content-Type=text/plain SomeField2=12345 SomeFieldWithNewLineAndTab=This is line1\r\nThis is line2\r\nThis is \ttab \ttab \ttab SomeFieldStartingWithAtSign=\@MyTwitterHandle
Filter	Enter filter to extract array from response. Example: $.rows[] --OR-- $.customers[].orders[*]. Check your response document and find out hierarchy you like to extract
Headers	Headers for Request. To enter multiple headers use double pipe or new line after each {header-name}:{value} pair

Other App Integration scenarios for Google BigQuery

SSIS

SQL Server

Power BI

SSRS

Informatica

MS Access

MS Excel

SSAS

Python

JAVA

SAP Crystal Reports

Azure Data Factory (ADF)

ODBC

Other Connectors for Tableau

Zendesk

Azure DevOps

ManagedEngine ServiceDesk Plus (Zoho)

Zoho CRM

Hubspot

Google Sheets

Stripe

YouTube

Google BigQuery

OneDrive

OData

Amazon Ads

Mailchimp

Jira

Outlook Mail (Office 365)

Zoom

Cosmos DB

Shopify

Nativo

Google Drive

SharePoint Online

Amazon MWS

ServiceNow

ElasticSearch

Google Calendar

DropBox

REST API

JSON

JSON File

XML

SOAP

XML File

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

Download Google BigQuery Connector for Tableau Documentation

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How to Read/Write Google BigQuery data in Tableau

Tableau Google BigQuery Connector

Create Data Source in ZappySys Data Gateway based on ZappySys API Driver

User Account [OAuth]

Service Account (Using Private Key File) [OAuth]

Create Project

Create Service Account

Give Permission to Service Account

Create Key (P12)

Manage Permissions / Give Access to Other Projects

Read data in SQL Server from the ZappySys Data Gateway data source

Read data in Tableau from SQL Server

Passing Parameters to REST API calls in Tableau (Dynamic SQL)

Firewall settings

Create Custom Store Procedure in ZappySys Driver

Create Custom Virtual Table in ZappySys Driver

Conclusion

Actions supported by Google BigQuery Connector

Other App Integration scenarios for Google BigQuery

Other Connectors for Tableau

How to connect Google BigQuery in Tableau?

How to get Google BigQuery data in Tableau?

How to read Google BigQuery data in Tableau?

How to load Google BigQuery data in Tableau?

How to import Google BigQuery data in Tableau?

How to pull Google BigQuery data in Tableau?

How to push data to Google BigQuery in Tableau?

How to write data to Google BigQuery in Tableau?

How to POST data to Google BigQuery in Tableau?

Call Google BigQuery API in Tableau

Consume Google BigQuery API in Tableau

Google BigQuery Tableau Automate

Google BigQuery Tableau Integration

Integration Google BigQuery in Tableau

Consume real-time Google BigQuery data in Tableau

Consume realtime Google BigQuery API data in Tableau

Google BigQuery ODBC Driver | ODBC Driver for Google BigQuery | ODBC Google BigQuery Driver | SSIS Google BigQuery Source | SSIS Google BigQuery Destination

Connect Google BigQuery in Tableau

Load Google BigQuery in Tableau

Load Google BigQuery data in Tableau

Read Google BigQuery data in Tableau

Google BigQuery API Call in Tableau