Google BigQuery Connector for Power BI

In this article you will learn how to integrate Google BigQuery data in Power BI without coding in just a few clicks (live / bi-directional connection to Google BigQuery). Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector.

Using Google BigQuery Connector you will be able to connect, read, and write data from within Power BI. Follow the steps below to see how we would accomplish that.

Download Documentation

Video Tutorial - Integrate Google BigQuery data in Power BI

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

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

Create ODBC Data Source (DSN) based on ZappySys API Driver

Step-by-step instructions

To get data from Google BigQuery using Power BI we first need to create a DSN (Data Source) which will access data from Google BigQuery. We will later be able to read data using Power BI. Perform these steps:

  1. Install ZappySys ODBC PowerPack.

  2. Open ODBC Data Sources (x64):
    Open ODBC Data Source

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

    ZappySys API Driver
    Create new User 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.

    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:

      Start creating a new project in Google Cloud
    3. Name your project and click CREATE button:

      Create a new project in Google Cloud
    4. Wait until the project is created:

      Wait until project is created in Google Cloud
    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:

      Select project in Google Cloud
    2. Then click the "hamburger" icon on the top left and access APIs & Services:

      Access APIs and services in Google Cloud
    3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      Enable API for project in Google Cloud
    4. In the search bar search for bigquery api and then locate and select BigQuery API:

      Search for API in Google Cloud
    5. If BigQuery API is not enabled, enable it:

      Enable Google BigQuery API
    6. Then repeat the step and enable Cloud Resource Manager API as well:

      Enable Cloud Resource Manager API
    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:

      View all products in Google Cloud
    2. Then access Google Auth Platform to start creating an OAuth application:

      Open Google Auth Platform in Google Cloud
    3. Start by pressing GET STARTED button:

      Start creating an app in Google Cloud
    4. Next, continue by filling in App name and User support email fields:

      Fill app info in Google Cloud
    5. Choose Internal option, if it's enabled, otherwise select External:

      Choose app audience in Google Cloud
    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:

      Add test user in Google Cloud app
    7. Then add your contact Email address:

      Enter app contact info in Google Cloud
    8. Finally, check the checkbox and click CREATE button:

      Create app in Google Cloud
    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:

      Start creating app client in Google Cloud
    2. Choose Desktop app as Application type and name your credentials:

      Create OAuth app client in Google Cloud
    3. Continue by opening the created credentials:

      View app client credentials in Google Cloud
    4. Finally, copy Client ID and Client secret for the later step:

      Use client ID and secret to read Google REST API data
    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)
    ODBC DSN Oauth Connection Configuration

    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:

      Start creating a new project in Google Cloud
    3. Name your project and click CREATE button:

      Create a new project in Google Cloud
    4. Wait until the project is created:

      Wait until project is created in Google Cloud
    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:

      Select project in Google Cloud
    2. Then click the "hamburger" icon on the top left and access APIs & Services:

      Access APIs and services in Google Cloud
    3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      Enable API for project in Google Cloud
    4. In the search bar search for bigquery api and then locate and select BigQuery API:

      Search for API in Google Cloud
    5. If BigQuery API is not enabled, enable it:

      Enable Google BigQuery API
    6. Then repeat the step and enable Cloud Resource Manager API as well:

      Enable Cloud Resource Manager API
    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:

      Access IAM & Admin in Google Cloud
    2. Once you do that, click Service Accounts on the left side and click CREATE SERVICE ACCOUNT button:

      Start creating service account in Google Cloud
    3. Then name your service account and click CREATE AND CONTINUE button:

      Create service account in Google Cloud
    4. Continue by clicking Select a role dropdown and start granting service account BigQuery Admin and Project Viewer roles:

      Start granting service account project roles in Google Cloud
    5. Find BigQuery group on the left and then click on BigQuery Admin role on the right:

      Grant service account BigQuery Admin role
    6. Then click ADD ANOTHER ROLE button, find Project group and select Viewer role:

      Grant service account project viewer role
    7. Finish adding roles by clicking CONTINUE button:

      Finish granting service account project roles in Google Cloud
      You can always add or modify permissions later in IAM & Admin.
    8. Finally, in the last step, just click button DONE:

      Finish configuring service account in Google Cloud
    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 (P12 certificate) to the created Service Account:

    1. In Service Accounts open newly created service account:

      Open service account in Google Cloud
    2. Next, copy email address of your service account for the later step:

      Copy service account email address in Google Cloud
    3. Continue by selecting KEYS tab, then press ADD KEY dropdown, and click Create new key menu item:

      Start creating key for service account in Google Cloud
    4. Finally, select P12 option and hit CREATE button:

      Create P12 key for service account in Google Cloud
    5. P12 certificate 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. *.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. *.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)
    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.

Video instructions

Read Google BigQuery data in Power BI using ODBC

Importing Google BigQuery data into Power BI from table or view

  1. Once you open Power BI Desktop click Get Data to get data from ODBC:
    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, finally, use extracted data from Google BigQuery in a Power BI report:
    Power Bi Extracted DSN Table Data

Importing Google BigQuery data into Power BI using SQL query

If you wish to import Google BigQuery data from SQL query rather than a table then you can use advanced options during import steps (as below). After selecting DSN you can click on advanced options to see SQL Query editor.

GoogleBigqueryDSN
                SELECT
                    ProductID,
                    ProductName,
                    SupplierID,
                    CategoryID,
                    QuantityPerUnit,
                    UnitPrice
                FROM Products
                WHERE UnitPrice > 20
            
Get REST API data in Power BI Desktop using SQL query and ODBC
Consider using Custom Objects feature in ODBC data source to encapsulate SQL query in a Virtual Table. This way, you can see a virtual table in Power BI table list where you can import multiple objects using the same connection rather than creating a new connection for each custom SQL query.

Using a full ODBC connection string

In the previous steps we used a very short format of ODBC connection string - a DSN. Yet sometimes you don't want a dependency on an ODBC data source (and an extra step). In those times, you can define a full connection string and skip creating an ODBC data source entirely. Let's see below how to accomplish that in the below steps:

  1. Open ODBC data source configuration and click Copy settings:
    ZappySys API Driver - Google BigQuery
    Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector
    GoogleBigqueryDSN
    Copy connection string for ODBC application
  2. The window opens, telling us the connection string was successfully copied to the clipboard: Successful connection string copying for ODBC application
  3. Then just paste the connection string into your script:
    GoogleBigqueryDSN
    DRIVER={ZappySys API Driver};ServiceUrl=https://www.googleapis.com/bigquery/v2;Provider=GoogleBigQuery;
    Use full connection string in Power BI Desktop to read API data
  4. You are good to go! The script will execute the same way as using a DSN.
The DSN defined in the Data source name (DSN) field will be ignored.

Have in mind that a full connection string has length limitations.

Proceed to the next step to find out the details.

Limitations of using a full connection string

Despite using a full ODBC connection string may be very convenient it comes with a limitation: it's length is limited to 1024 symbols (or sometimes more). It usually happens when API provider generates a very long Refresh Token when OAuth is at play. If you are using such a long ODBC connection string, you may get this error:

"Connection string exceeds maximum allowed length of 1024"

But there is a solution to this by storing the full connection string in a file. Follow the steps below to achieve this:

  1. Open your ODBC data source.
  2. Click Copy settings button to copy a full connection string (see the previous section on how to accomplish that).
  3. Then create a new file, let's say, in C:\temp\odbc-connection-string.txt.
  4. Continue by pasting the copied connection string into a newly created file and save it.
  5. Finally, the last step! Just construct a shorter ODBC connection string using this format:
    DRIVER={ZappySys API Driver};SettingsFile=C:\temp\odbc-connection-string.txt
  6. Our troubles are over! Now you should be able to use this connection string in Power BI with no problems.
This feature requires ODBC PowerPack v1.9.0 or later.

Editing query for table in Power BI

There will be a time you need to change the initial query after importing data into Power BI. Don't worry, just right-click on your table and click Edit query menu item:

Edit query in Power BI to get REST API data
Refer to Power Query M reference for more information on how to use its advanced features in your queries.

Using parameters in Power BI (dynamic query)

In the real world, many values of your REST / SOAP API call may be coming from parameters. If that's the case for you can try to edit script manually as below. In below example its calling SQL Query with POST method and passing some parameters. Notice below where paraAPIKey is Power BI Parameter (string type). You can use parameters anywhere in your script just like the normal variable.

To use a parameter in Power BI report, follow these simple steps:

  1. Firstly, you need to Edit query of your table (see previous section)

  2. Then just create a new parameter by clicking Manage Parameters dropdown, click New Parameter option, and use it in the query:

    						
                                = Odbc.Query("dsn=GoogleBigqueryDSN",
                                             "SELECT ProductID, ProductName, UnitPrice, UnitsInStock
                                              FROM Products
                                              WHERE UnitPrice > " & Text.From(MyParameter) & "
                                              ORDER BY UnitPrice")
                            
                    
    Use parameter in Power BI to get REST API data
    Refer to Power Query M reference for more information on how to use its advanced features in your queries.

Using DirectQuery Option rather than Import

So far we have seen how to Import Google BigQuery data into Power BI but what if you have too much data and you dont want to import but link it. Power BI Offers very useful feature for this scenario. Its called DirectQuery Option. In this section we will explore how to use DirectQuery along with ZappySys Drivers.

Out of the box ZappySys Drivers wont work in ODBC Connection Mode so you have to use SQL Server Connection rather than ODBC if you wish to use Live data using DirectQuery option. See below step by step instructions to enable DirectQuery mode in Power BI for Google BigQuery data.

Basically we will use ZappySys Data Gateway its part of ODBC PowerPack. We will then use Linked Server in SQL Server to Link API Service and then we will issue OPENROWSET queries from Power BI to SQL Server and it will then call Google BigQuery via ZappySys Data Gateway.

  1. First read this article carefully, How to query Google BigQuery API in SQL Server.
  2. Once linked server is configured we are ready to issue API query in Power BI.
  3. Click Get Data in Power BI, select SQL Server Database
  4. Enter your server name and any database name
  5. Select Mode as DirectQuery
  6. Click on Advanced and enter query like below (we are assuming you have created Google BigQuery Data Source in Data Gateway and defined linked server (Change name below).
    SELECT * FROM OPENQUERY([GOOGLE_BIGQUERY_LINKED_SERVER], 'SELECT * FROM Customers')
    SELECT * FROM OPENQUERY([GOOGLE_BIGQUERY_LINKED_SERVER], 'SELECT * FROM Customers')
    DirectQuery option for Power BI (Read Google BigQuery Data Example using SQL Server Linked Server and ZappySys Data Gateway)


    DirectQuery option for Power BI (Read Google BigQuery Data Example using SQL Server Linked Server and ZappySys Data Gateway)

  7. Click OK and Load data ... That's it. Now your Google BigQuery API data is linked rather than imported.

Publishing Power BI report to Power BI service

Here are the instructions on how to publish a Power BI report to Power BI service from Power BI Desktop application:

  1. First of all, go to Power BI Desktop, open a Power BI report, and click Publish button:

    Publish Power BI report to Power BI service
  2. Then select the Workspace you want to publish report to and hit Select button:

    Publish Power BI report to workspace
  3. Finally, if everything went right, you will see a window indicating success:

    Successful Power BI report publishing

    If you need to periodically refresh Power BI semantic model (dataset) to ensure data accuracy and up-to-dateness, you can accomplish that by using Microsoft On-premises data gateway. Proceed to the next section - Refreshing Power BI semantic model (dataset) using On-premises data gateway - and learn how to do that.

Refreshing Power BI semantic model (dataset) using On-premises data gateway

Power BI allows to refresh semantic models which are based on data sources that reside on-premises. This can be achieved using Microsoft On-premises data gateway. There are two types of On-premises gateways:

  • Standard Mode
  • Personal Mode

Standard Mode supports Power BI and other Microsoft Data Fabric services. It fits perfectly for Enterprise solutions as it installs as a Windows Service and also supports Direct Query feature.

Personal Mode, on the other hand, can be configured faster, but is designed more for home users (you cannot install it as a Windows Service and it does not support DirectQuery). You will find a detailed comparison in the link above.

We recommend to go with Personal Mode for a quick POC solution, but use Standard Mode in production environment.

Below you will find instructions on how to refresh semantic model using both types of gateways.

Refresh using On-premises data gateway (standard mode)

Here are the instructions on how to refresh a Power BI semantic model using On-premises data gateway (standard mode):

  1. Go to Power BI My workspace, hover your mouse cursor on your semantic model and click Settings:

    Configure Power BI semantic model settings
  2. If you see this view, it means you have to install On-premises data gateway (standard mode):

    On-premises data gateway is not installed
  3. Install On-premises data gateway (standard mode) and sign-in:

    signing in into on-premises data gateway standard
    Use the same email address you use when logging in into Power BI account.
  4. Register a new gateway (or migrate an existing one):

    registering or migrating on-premises data gateway standard
  5. If you are creating a new gateway, name your gateway, enter a Recovery key, and click Configure button:

    naming on-premises data gateway standard
  6. Now, let's get back to your semantic model settings in Power BI portal. Refresh the page and you should see your newly created gateway. Click arrow icon and then click on Add to gateway link:

    ODBC{"connectionstring":"dsn=GoogleBigqueryDSN"}
    Using On-premises Data Gateway Standard for Power BI Semantic Model
  7. Once you do that, you will create a new gateway connection. Give it a name, set Authentication method, Privacy level, and click Create button:

    dsn=GoogleBigqueryDSN
    Create new connection in Power BI On-premises data gateway
    In this example, we used the least restrictive Privacy level.

    If your connection uses a full connection string you may hit a length limitation when entering it into the field. To create the connection, you will need to shorten it manually. Check the section about the limitation of a full connection string on how to accomplish it.

    On-premises data gateway (personal mode) does not have this limitation.

  8. Proceed by choosing the newly created connection:

    ODBC{"connectionstring":"dsn=GoogleBigqueryDSN"}
    Selecting gateway connection in Power BI semantic model
  9. Finally, you are at the final step where you can refresh the semantic model:

    Refreshing Power BI semantic model using On-premises Data Gateway

Refresh using On-premises data gateway (personal mode)

Here are the instructions on how to refresh a Power BI semantic model using On-premises data gateway (personal mode):

  1. Go to Power BI My workspace, hover your mouse cursor on your semantic model and click Settings:

    Configure Power BI semantic model settings
  2. If you see this view, it means you have to install On-premises data gateway (personal mode):

    On-premises data gateway is not installed
  3. Install On-premises data gateway (personal mode) and sign-in:

    Sign-in to On-premises data gateway personal
    Use the same email address you use when logging in into Power BI account.
  4. Again, go to your semantic model Settings, expand Data source credentials, click Edit credentials, select Authentication method together with Privacy level, and then click Sign in button:

    dsn=GoogleBigqueryDSN
    Use On-premises data gateway personal for Power BI semantic model
  5. Finally, you are ready to refresh your semantic model:

    Refreshing Power BI semantic model using On-premises Data Gateway

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:
    ZappySys Driver - Add Stored Procedure

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

  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>';
    

    ZappySys Driver - Create Custom Stored Procedure

  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';

    ZappySys Driver - Execute Custom Stored Procedure

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

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

    ZappySys Driver - Generate SQL Server Query

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

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

    ZappySys Driver - Generate SQL Server Query

  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.
    ZappySys Driver - Generate SQL Server Query

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
Filter
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
Filter
 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 Set 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
Option Value
No filter
Example1 $.store.books[*]
Example2 (Sections Under Books) $.store.books[*].sections[*]
Example3 (Equals) $.store.books[?(@author=='sam')]
Example4 (Equals - Any Section) $..[?(@author=='sam')]
Example5 (Not Equals - Any Section) $..[?(@author!='sam')]
Example6 (Number less than) $.store.books[?(@.price<10)] Example7 (Regular Expression - Contains Pattern)=$.store.books[?(@author=~ /sam|bob/ )]
Example8 (Regular Expression - Does Not Contain Pattern) $.store.books[?(@author=~ /^((?!sam|bob).)*$/ )]
Example9 (Regular Expression - Exact Pattern Match) $.store.books[?(@author=~ /^sam|bob$/ )]
Example10 (Regular Expression - Starts With) $.store.books[?(@author=~ /^sam/ )]
Example11 (Regular Expression - Ends With) $.store.books[?(@author=~ /sam$/ )]
Example12 (Between) $.store.employees[?( @.hiredate>'2015-01-01' && @.hiredate<'2015-01-04' )]
Headers Headers for Request. To enter multiple headers use double pipe or new line after each {header-name}:{value} pair
 Generic Request (Bulk Write)
This is a generic endpoint for bulk write purpose. 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
IsMultiPart Set 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): Delete Multiple Records (Call DML)    [Read more...]

This Server side SQL query example of BigQuery shows how to invoke DELETE statement. To do that 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 DELETE FROM TestDataset.MyTable Where Id > 5

Native Query (ServerSide): Query with CAST unix TIMESTAMP datatype column as datetime    [Read more...]

This example shows how to query timestamp column as DateTime. E.g. 73833719.524272 should be displayed as 1972-05-04 or with milliseconds 1972-05-04 1:21:59.524 PM then use CAST function (you must use #DirectSQL prefix)

#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

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 
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')

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).

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