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  Help File 

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

To get data from GoogleBigQuery using Power BI we first need to create a DSN (Data Source) which will access data from GoogleBigQuery. 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):
    Open ODBC Data Source

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

    ZappySys API Driver
    Create new System DSN for 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
    ODBC DSN Template Selection

  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.

    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. 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 (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...
    ODBC DSN Oauth Connection Configuration
    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...
    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...
    ODBC DSN Oauth Connection Configuration

  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:
    ODBC ZappySys Data Source Preview

  7. Click OK to finish creating the data source.

Read Google BigQuery data in Power BI from ODBC

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

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

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

    GoogleBigQueryDSN
    Power Bi Select ZappySys Driver DSN

  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
    Power Bi DSN Authenticaation

  5. Finally, you will be asked to select a table or view to get data from. Select one and load the data!
    Power Bi Load DSN Table Data

  6. Finally, use data extracted from Google BigQuery API in Power BI reports:
    Power Bi Extracted DSN Table Data

Import Google BigQuery data into Power BI from SQL Query

If you wish to import Google BigQuery data from SQL query rather than selecting table name 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.

Import Google BigQuery data into Power BI using SQL Query (ODBC Data source)

Import Google BigQuery data into Power BI using SQL Query (ODBC Data source)

If you type invalid SQL, Power BI may revert to table mode rather than import from Query. Make sure you do not use "$" it as a table name in SELECT...FROM $. You can use "_root_" instead (e.g., SELECT .. FROM _root_). Consider using Custom Object to wrap custom SQL in a Virtual Table. This way, you can see a virtual table in Table mode where you can import multiple objects using the same connection rather than creating a new connection for each custom SQL.

Edit Query / 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.

Import Google BigQuery in Power BI - Using parameters in SQL Query (Edit code - Advanced Mode)

 

let
    vKey=paraAPIKey,
    Source = Odbc.Query(
    "dsn=ZS-OData Customers", 
    "SELECT * FROM value WITH (SRC='http://httpbin.org/post'," 
    & "METHOD='POST'," 
    & "HEADER='Content-Type:application/json'," 
    & "BODY=@'{""CallerId"":1111, ""ApiKey"":""" & vKey & """}')")
in
Source

Edit Query Settings after Import

There will be a time you need to change initial Query after dataset import in Power BI. Not to worry, just follow these steps to edit your SQL.

Edit Power BI Data Source SQL after SOAP Web Service Import

Edit Power BI Data Source SQL after REST / SOAP Web Service Import

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.

Step-By-Step - How to query Google BigQuery API in SQL Server

  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)


    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.

Working with Gateways in Power BI (Schedule Import)

If the data needs to be updated, it is necessary to create a gateway on-premises. In this new section, we will install a Power BI Gateway and in the next section schedule it to update the Google BigQuery information.
  1. In the last section, we Published the report. Power BI may ask you to SIGN IN.
    Sign in Power BI

    Sign in Power BI

  2. Select the Workspace and select Datasets
    Go do workspace and select dataset

    Go to workspace and dataset

  3. Right-click the report and select Settings.
    Define settings for Power BI report

    Specify settings for the report

  4. The system will ask for a Gateway. Stay here.
    add power bi gateway

    Add Power BI Gateway

  5. Use the following link to install a Data Gateway:
    https://docs.microsoft.com/en-us/power-bi/service-gateway-onprem
  6. Run the installer and press Next
    Initial gateway window for installation

    Gateway installer

  7. Select the option On-premises data gateway (recommended). This option allows access to multiple users and can be used by more applications than Power BI.
    Choose Power BI gateway

    Choose Power BI option

  8. The installer will show a warning message.
    Warning message gateway installation

    Warning message during installation

  9. Select the path to install and check the I accept the terms.
    accept terms

    Accept terms

  10. Specify the email address to use the gateway.
    Register email in gateway installation

    Register email address

  11. After entering the email, write the gateway name and a recovery key. Make sure to confirm the recovery key.
    Specify name and recovery key

    Enter recovery key

Manage gateways and configure the schedule

Once that the gateway is installed we will configure it and add the connection strings.
  1. The next step is to go to manage gateway
    Power BI - Manage Gateway Setting

    Power BI - Manage Gateway Setting

  2. In order to get the connection string, we will need the connection string of the ZappySys API Driver. In the first section of this post, we explained how to configure it. Press Copy Connection String
    ZappySys connection properties

    Connection properties

  3. Once that the data is copied, add a New data Source. In Data Source Name, enter the Data Source Name of the ZappySys API Driver in step 13 and in Data Source Type, select ODBC. In connection string copy and paste from the clipboard of the step 13 and press Add.
    Zappy connection properties

    ZappySys connection properties in Power BI

  4. Once added the gateway. You can see the schedule refresh to On and Add another time to add the time where you want to refresh the data.
    Schedule gateway

    Schedule 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

  1. Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
    ZappySys Driver - Add Store Procedure

  2. Enter the desired Procedure name and click on OK:
    ZappySys Driver - Add Store Procedure Name

  3. 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>';
    

    ZappySys Driver - Create Custom Store Procedure

  4. 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';

    ZappySys Driver - Execute Custom Store Procedure

  5. 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 @fromdate=''1996-07-30''')

    ZappySys Driver - Generate SQL Server Query

  6. Now go to SQL served and execute that query and it will make the API call using store procedure and provide you the response.
    ZappySys Driver - Generate SQL Server Query

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:
    ZappySys Driver - Add Table

  2. Enter the desired Table name and click on OK:
    ZappySys Driver - Add Table Name

  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'

    ZappySys Driver - Create Custom Table

  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"

    ZappySys Driver - Execute Custom Virtual Table Query

  6. 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''')

    ZappySys Driver - Generate SQL Server Query

  7. Now go to SQL served and execute that query and it will make the API call using store procedure and provide you the response.
    ZappySys Driver - Generate SQL Server Query

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 

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    [Read more...]
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.    [Read more...]
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 .    [Read more...]
Parameter Description
 List Projects
Lists Projects that the caller has permission on and satisfy the specified filter.    [Read more...]
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.    [Read more...]
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.    [Read more...]
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.    [Read more...]
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
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.    [Read more...]
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.    [Read more...]
Parameter Description
ProjectId
DatasetId
 Get Query Schema (From SQL)
Runs a BigQuery SQL query synchronously and returns query schema    [Read more...]
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.    [Read more...]
Parameter Description
DatasetId
TableId
 insert_table_data
   [Read more...]
Parameter Description
ProjectId
DatasetId
TableId
 post_[$parent.tableReference.datasetId$]_[$parent.tableReference.tableId$]
   [Read more...]
 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.    [Read more...]
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

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.

Native Query (ServerSide): Query using Simple SQL    [Read more...]

Server side BigQuery SQL query example. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL). Query free dataset table (bigquery-public-data.samples.wikipedia)

#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) */

Native Query (ServerSide): Query using Complex SQL    [Read more...]

Server side SQL query example of BigQuery. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL). Query free dataset table (bigquery-public-data.usa_names.usa_1910_2013)

#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

Native Query (ServerSide): Create Table / Run Other DDL    [Read more...]

Example of how to run Valid BigQuery DDL statement. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL)

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

Native Query (ServerSide): UPDATE Table data for complex types (e.g. Nested RECORD, Geography, JSON)    [Read more...]

Example of how to run Valid BigQuery DML statement ()e.g. UPDATE / INSERT / DELETE). This usecase shows how to update record with complex data types such as RECORD (i.e Array), Geography, JSON and more. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL)

#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

Native Query (ServerSide): DROP Table (if exists) / Other DDL    [Read more...]

Example of how to run Valid BigQuery DDL statement. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL)

#DirectSQL DROP TABLE IF EXISTS Myproject.Mydataset.Mytable

Native Query (ServerSide): Call Stored Procedure    [Read more...]

Example of how to run BigQuery Stored Procedure and pass parameters. Assuming you created a valid stored proc called usp_GetData in TestDataset, call like below.

#DirectSQL CALL TestDataset.usp_GetData(1)

INSERT Single Row    [Read more...]

This is sample how you can insert into BigQuery using ZappySys query language. You can also use ProjectId='myproject-id' in WITH clause.

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

INSERT Multiple Rows from SQL Server    [Read more...]

This example shows how to bulk insert into Google BigQuery Table from microsoft SQL Server as external source. Notice that INSERT is missing column list. Its provided by source query so must produce valid column names found in target BQ Table (you can use SQL Alias in Column name to produce matching names)

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 Multiple Rows from any ODBC Source (DSN)    [Read more...]

This example shows how to bulk insert into Google BigQuery Table from any external ODBC Source (Assuming you have installed ODBC Driver and configured DSN). Notice that INSERT is missing column list. Its provided by source query so it must produce valid column names found in target BQ Table (you can use SQL Alias in Column name to produce matching names)

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

INSERT Multiple Rows from any JSON Files / API (Using ZappySys ODBC JSON Driver)    [Read more...]

This example shows how to bulk insert into Google BigQuery Table from any external ODBC JSON API / File Source (Assuming you have installed ZappySys ODBC Driver for JSON). Notice that INSERT is missing column list. Its provided by source query so it must produce valid column names found in target BQ Table (you can use SQL Alias in Column name to produce matching names). You can also use similar approach to read from CSV files or XML Files. Just use CSV / XML driver rather than JSON driver in connection string. Refer this for more examples of JSON Query https://zappysys.com/onlinehelp/odbc-powerpack/scr/json-odbc-driver-sql-query-examples.htm

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='*')

List Projects    [Read more...]

Lists Projects for which user has access

SELECT * FROM list_projects

List Datasets    [Read more...]

Lists Datasets for specified project. If you do not specify ProjectId then it will use connection level details.

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

List Tables    [Read more...]

Lists tables for specified project / dataset. If you do not specify ProjectId or datasetId then it will use connection level details.

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

Delete dataset    [Read more...]

Delete dataset for specified ID. If you like to delete all tables under that dataset then set deleteContents='true'

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

Other App Integration scenarios for Google BigQuery

Other Connectors for Power BI


Download Google BigQuery Connector for Power BI Documentation 

  • How to connect Google BigQuery in Power BI?

  • How to get Google BigQuery data in Power BI?

  • How to read Google BigQuery data in Power BI?

  • How to load Google BigQuery data in Power BI?

  • How to import Google BigQuery data in Power BI?

  • How to pull Google BigQuery data in Power BI?

  • How to push data to Google BigQuery in Power BI?

  • How to write data to Google BigQuery in Power BI?

  • How to POST data to Google BigQuery in Power BI?

  • Call Google BigQuery API in Power BI

  • Consume Google BigQuery API in Power BI

  • Google BigQuery Power BI Automate

  • Google BigQuery Power BI Integration

  • Integration Google BigQuery in Power BI

  • Consume real-time Google BigQuery data in Power BI

  • Consume realtime Google BigQuery API data in Power BI

  • 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 Power BI

  • Load Google BigQuery in Power BI

  • Load Google BigQuery data in Power BI

  • Read Google BigQuery data in Power BI

  • Google BigQuery API Call in Power BI