How to integrate Google BigQuery using PowerShell

Learn how to quickly and efficiently connect Google BigQuery with PowerShell for smooth data access.

Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required. You can do it all using the high-performance Google BigQuery ODBC Driver for PowerShell (often referred to as the Google BigQuery Connector). We'll walk you through the entire setup.

Ready to dive in? Download the product to jump right in, or follow the step-by-step guide below to see how it works.

Create data source using Google BigQuery ODBC Driver

Step-by-step instructions

To get data from Google BigQuery using PowerShell, we first need to create an ODBC data source. We will later read this data in PowerShell. Perform these steps:

1. Download and install ODBC PowerPack (if you haven't already).

2. Search for odbc and open the ODBC Data Sources (64-bit):

   

3. Create a User data source (User DSN) based on the ZappySys API Driver driver:

   ZappySys API Driver

   
   • Create and use a User DSN if the client application runs under a User Account. This is the ideal option at design time (e.g., when developing in Visual Studio). Use it for both types of applications (64-bit and 32-bit).
   • Create and use a System DSN if the client application runs under a System Account (e.g., as a Windows Service). This is usually the required option in a production environment. If your Windows Service is a 32-bit application, you must use the 32-bit ODBC Data Source Administrator to configure this

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

   

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 Account

   Google BigQuery authentication

   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]

   Follow these steps on how to create Client Credentials (User Account principle) to authenticate and access BigQuery API in SSIS package or ODBC data source:

   WARNING: If you are planning to automate processes, we recommend that you use a Service Account authentication method. In case, you still need to use User Account, then make sure you use a system/generic account (e.g. automation@my-company.com). When you use a personal account which is tied to a specific employee profile and that employee leaves the company, the token may become invalid and any automated processes using that token will start to fail.

   Step-1: Create project

   This step is optional, if you already have a project in Google Cloud and can use it. However, if you don't, proceed with these simple steps to create one:

   1. First of all, go to Google API Console.

   2. Then click Select a project button and then click NEW PROJECT button:

      

   3. Name your project and click CREATE button:

      

   4. Wait until the project is created:

      
   5. Done! Let's proceed to the next step.

   Step-2: Enable Google Cloud APIs

   In this step we will enable BigQuery API and Cloud Resource Manager API:

   1. Select your project on the top bar:

      

   2. Then click the "hamburger" icon on the top left and access APIs & Services:

      

   3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      

   4. In the search bar search for bigquery api and then locate and select BigQuery API:

      

   5. If BigQuery API is not enabled, enable it:

      

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

      
   7. Done! Let's proceed to the next step.

   Step-3: Create OAuth application

   1. First of all, click the "hamburger" icon on the top left and then hit VIEW ALL PRODUCTS:

      

   2. Then access Google Auth Platform to start creating an OAuth application:

      

   3. Start by pressing GET STARTED button:

      

   4. Next, continue by filling in App name and User support email fields:

      

   5. Choose Internal option, if it's enabled, otherwise select External:

      

   6. Optional step if you used Internal option in the previous step. Nevertheless, if you had to use External option, then click ADD USERS to add a user:

      

   7. Then add your contact Email address:

      

   8. Finally, check the checkbox and click CREATE button:

      
   9. Done! Let's create Client Credentials in the next step.

   Step-4: Create Client Credentials

   1. In Google Auth Platform, select Clients menu item and click CREATE CLIENT button:

      

   2. Choose Desktop app as Application type and name your credentials:

      

   3. Continue by opening the created credentials:

      

   4. Finally, copy Client ID and Client secret for the later step:

      

   5. Done! We have all the data needed for authentication, let's proceed to the last step!

   Step-5: Configure connection

   1. Now go to SSIS package or ODBC data source and use previously copied values in User Account authentication configuration:

      • In the ClientId field paste the Client ID value.
      • In the ClientSecret field paste the Client secret value.

   2. Press Generate Token button to generate Access and Refresh Tokens.

   3. Then choose ProjectId from the drop down menu.

   4. Continue by choosing DatasetId from the drop down menu.

   5. Finally, click Test Connection to confirm the connection is working.

   6. Done! Now you are ready to use Google BigQuery Connector!

   API Connection Manager configuration

   Just perform these simple steps to finish authentication configuration:

   1. Set Authentication Type to User Account [OAuth]
   2. Optional step. Modify API Base URL if needed (in most cases default will work).
   3. Fill in all the required parameters and set optional parameters if needed.
   4. Press Generate Token button to generate the tokens.
   5. Finally, hit OK button:

   GoogleBigqueryDSN

   Google BigQuery

   User Account [OAuth]

   https://www.googleapis.com/bigquery/v2

   Required Parameters
   UseCustomApp	Fill-in the parameter...
   ProjectId (Choose after [Generate Token] clicked)	Fill-in the parameter...
   DatasetId (Choose after [Generate Token] clicked and ProjectId selected)	Fill-in the parameter...
   Optional Parameters
   ClientId
   ClientSecret
   Scope	https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write
   RetryMode	RetryWhenStatusCodeMatch
   RetryStatusCodeList	429|503
   RetryCountMax	5
   RetryMultiplyWaitTime	True
   Job Location
   Redirect URL (Only for Web App)

   

   Service Account (Using *.json OR *.p12 key file)

   Google BigQuery authentication

   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]

   Follow these steps on how to create Service Account to authenticate and access BigQuery API in SSIS package or ODBC data source:

   Step-1: Create project

   This step is optional, if you already have a project in Google Cloud and can use it. However, if you don't, proceed with these simple steps to create one:

   1. First of all, go to Google API Console.

   2. Then click Select a project button and then click NEW PROJECT button:

      

   3. Name your project and click CREATE button:

      

   4. Wait until the project is created:

      
   5. Done! Let's proceed to the next step.

   Step-2: Enable Google Cloud APIs

   In this step we will enable BigQuery API and Cloud Resource Manager API:

   1. Select your project on the top bar:

      

   2. Then click the "hamburger" icon on the top left and access APIs & Services:

      

   3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      

   4. In the search bar search for bigquery api and then locate and select BigQuery API:

      

   5. If BigQuery API is not enabled, enable it:

      

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

      
   7. Done! Let's proceed to the next step and create a service account.

   Step-3: Create Service Account

   Use the steps below to create a Service Account in Google Cloud:

   1. First of all, go to IAM & Admin in Google Cloud console:

      

   2. Once you do that, click Service Accounts on the left side and click CREATE SERVICE ACCOUNT button:

      

   3. Then name your service account and click CREATE AND CONTINUE button:

      

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

      

   5. Find BigQuery group on the left and then click on BigQuery Admin role on the right:

      

   6. Then click ADD ANOTHER ROLE button, find Project group and select Viewer role:

      

   7. Finish adding roles by clicking CONTINUE button:

      
      You can always add or modify permissions later in IAM & Admin.

   8. Finally, in the last step, just click button DONE:

      

   9. Done! We are ready to add a Key to this service account in the next step.

   Step-4: Add Key to Service Account

   We are ready to add a Key (JSON or P12 key file) to the created Service Account:

   1. In Service Accounts open newly created service account:

      

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

      

   3. Continue by selecting KEYS tab, then press ADD KEY dropdown, and click Create new key menu item:

      

   4. Finally, select JSON (Engine v19+) or P12 option and hit CREATE button:

      
   5. Key file downloads into your machine. We have all the data needed for authentication, let's proceed to the last step!

   Step-5: Configure connection

   1. Now go to SSIS package or ODBC data source and configure these fields in Service Account authentication configuration:

      • In the Service Account Email field paste the service account Email address value you copied in the previous step.
      • In the Service Account Private Key Path (i.e. *.json OR *.p12) field use downloaded certificate's file path.
   2. Done! Now you are ready to use Google BigQuery Connector!

   API Connection Manager configuration

   Just perform these simple steps to finish authentication configuration:

   1. Set Authentication Type to Service Account (Using *.json OR *.p12 key file) [OAuth]
   2. Optional step. Modify API Base URL if needed (in most cases default will work).
   3. Fill in all the required parameters and set optional parameters if needed.
   4. Finally, hit OK button:

   GoogleBigqueryDSN

   Google BigQuery

   Service Account (Using *.json OR *.p12 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. *.json OR *.p12)	Fill-in the parameter...
   ProjectId	Fill-in the parameter...
   DatasetId (Choose after ProjectId)	Fill-in the parameter...
   Optional Parameters
   Scope	https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write
   RetryMode	RetryWhenStatusCodeMatch
   RetryStatusCodeList	429
   RetryCountMax	5
   RetryMultiplyWaitTime	True
   Job Location
   Impersonate As (Enter Email Id)

   

6. Once the data source connection has been configured, it's time to configure the SQL query. Select the Preview tab and then click Query Builder button to configure the SQL query:

   

   ZappySys API Driver - Google BigQuery

   Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required.

   GoogleBigqueryDSN

   

7. Start by selecting the Table or Endpoint you are interested in and then configure the parameters. This will generate a query that we will use in PowerShell to retrieve data from Google BigQuery. Hit OK button to use this query in the next step.

   #DirectSQL
   SELECT *
   FROM bigquery-public-data.samples.wikipedia
   LIMIT 1000

   
   Some parameters configured in this window will be passed to the Google BigQuery API, e.g. filtering parameters. It means that filtering will be done on the server side (instead of the client side), enabling you to get only the meaningful data much faster.

8. Now hit Preview Data button to preview the data using the generated SQL query. If you are satisfied with the result, use this query in PowerShell:

   

   ZappySys API Driver - Google BigQuery

   Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required.

   GoogleBigqueryDSN

   #DirectSQL
   SELECT *
   FROM bigquery-public-data.samples.wikipedia
   LIMIT 1000

   
   You can also access data quickly from the tables dropdown by selecting <Select table>.
   A WHERE clause, LIMIT keyword will be performed on the client side, meaning that the whole result set will be retrieved from the Google BigQuery API first, and only then the filtering will be applied to the data. If possible, it is recommended to use parameters in Query Builder to filter the data on the server side (in Google BigQuery servers).

9. Click OK to finish creating the data source.

Video Tutorial

Read Google BigQuery data in PowerShell

Sometimes, you need to quickly access and work with your Google BigQuery data in PowerShell. Whether you need a quick data overview or the complete dataset, this article will guide you through the process. Here are some common scenarios:

• Quickly peek at Google BigQuery data
• Monitor data constantly in your console

• Export data to a CSV file so that it can be sliced and diced in Excel
• Export data to a JSON file so that it can ingested by other processes
• Export data to an HTML file for user-friendly view and easy sharing
• Create a schedule to make it an automatic process

• Store data internally for analysis or for further ETL processes
• Create a schedule to make it an automatic process

• Integrate data with other systems via external APIs

In this article, we will delve deeper into how to quickly view the data in PowerShell terminal and how to save it to a file. But let's stop talking and get started!

Reading individual fields

1. Open your favorite PowerShell IDE (we are using Visual Studio Code).

2. Use this code snippet to read the data using GoogleBigqueryDSN data source:

   "DSN=GoogleBigqueryDSN"

   

   For your convenience, here is the whole PowerShell script:

   # Configure connection string and query
   $connectionString = "DSN=GoogleBigqueryDSN"
   $query = "SELECT * FROM Customers"
   
   # Instantiate OdbcDataAdapter and DataTable
   $adapter = New-Object System.Data.Odbc.OdbcDataAdapter($query, $connectionString)
   $table = New-Object System.Data.DataTable
   
   # Fill the table with data
   $adapter.Fill($table)
   
   # Since we know we will be reading just 4 columns, let's define format for those 4 columns, each separated by a tab
   $format = "{0}`t{1}`t{2}`t{3}"
   
   # Display data in the console
   foreach ($row in $table.Rows)
   {
       # Construct line based on the format and individual Google BigQuery fields
       $line = $format -f ($row["CustomerId"], $row["CompanyName"], $row["Country"], $row["Phone"])
       Write-Host $line
   }
   

   Access specific Google BigQuery table field using this code snippet:

   $field = $row["ColumnName"]

   You will find more info on how to manipulate DataTable.Rows property in Microsoft .NET reference.
   For demonstration purposes we are using sample tables which may not be available in Google BigQuery.

3. To read values in a console, save the script to a file and then execute this command inside PowerShell terminal:

   
   You can also use even a simpler command inside the terminal, e.g.:

   . 'C:\Users\john\Documents\dsn.ps1'

Retrieving all fields

However, there might be case, when you want to retrieve all columns of a query. Here is how you do it:

"DSN=GoogleBigqueryDSN"

Again, for your convenience, here is the whole PowerShell script:

# Configure connection string and query
$connectionString = "DSN=GoogleBigqueryDSN"
$query = "SELECT CustomerId, CompanyName, Country, Phone FROM Customers"

# Instantiate OdbcDataAdapter and DataTable
$adapter = New-Object System.Data.Odbc.OdbcDataAdapter($query, $connectionString)
$table = New-Object System.Data.DataTable

# Fill the table with data
$adapter.Fill($table)

# Display data in the console
foreach ($row in $table.Rows) {
    $line = ""
    foreach ($column in $table.Columns) {
        $value = $row[$column.ColumnName]

        # Let's handle NULL values
        if ($value -is [DBNull])
        {
            $value = "(NULL)"
        }
        $line += $value + "`t"
    }
    Write-Host $line
}

SELECT * FROM Customers LIMIT 10

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 - Configuration [Version: 2.0.1.10418]

   ZappySys API Driver - Google BigQuery

   Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required.

   GoogleBigqueryDSN

   
2. The window opens, telling us the connection string was successfully copied to the clipboard:
3. Then just paste the connection string into your script:
4. You are good to go! The script will execute the same way as using a DSN.

Handling 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 PowerShell with no problems.

Write Google BigQuery data to a file in PowerShell

Save data to a CSV file

Export data to a CSV file so that it can be sliced and diced in Excel:

# Configure connection string and query
$connectionString = "DSN=GoogleBigqueryDSN"
$query = "SELECT * FROM Customers"

# Instantiate OdbcDataAdapter and DataTable
$adapter = New-Object System.Data.Odbc.OdbcDataAdapter($query, $connectionString)
$table = New-Object System.Data.DataTable

# Fill the table with data
$adapter.Fill($table)

# Export table data to a file
$table | ConvertTo-Csv -NoTypeInformation -Delimiter "`t" | Out-File "C:\Users\john\saved-data.csv" -Force

Save data to a JSON file

Export data to a JSON file so that it can ingested by other processes (use the above script, but change this part):

# Export table data to a file
$table | ConvertTo-Json | Out-File "C:\Users\john\saved-data.json" -Force

Save data to an HTML file

Export data to an HTML file for user-friendly view and easy sharing (use the above script, but change this part):

# Export table data to a file
$table | ConvertTo-Html | Out-File "C:\Users\john\saved-data.html" -Force

Optional: Centralized data access via ZappySys Data Gateway

In some situations, you may need to provide Google BigQuery data access to multiple users or services. Configuring the data source on a Data Gateway creates a single, centralized connection point for this purpose.

This configuration provides two primary advantages:

• Centralized data access
  The data source is configured once on the gateway, eliminating the need to set it up individually on each user's machine or application. This significantly simplifies the management process.
• Centralized access control
  Since all connections route through the gateway, access can be governed or revoked from a single location for all users.

To achieve this, you must first create a data source in the Data Gateway (server-side) and then create an ODBC data source in PowerShell (client-side) to connect to it.

Let's not wait and get going!

Create Google BigQuery data source in the gateway

In this section we will create a data source for Google BigQuery in the Data Gateway. Let's follow these steps to accomplish that:

1. Search for gateway in the Windows Start Menu and open ZappySys Data Gateway Configuration:

   

2. Go to the Users tab and follow these steps to add a Data Gateway user:

   • Click the Add button
   • In the Login field enter a username, e.g., john
   • Then enter a Password
   • Check the Is Administrator checkbox
   • Click OK to save
   

3. Now we are ready to add a data source:

   • Click the Add button
   • Give the Data source a name (have it handy for later)
   • Then select Native - ZappySys API Driver
   • Finally, click OK
   GoogleBigqueryDSN

   ZappySys API Driver

   

4. When the ZappySys API Driver configuration window opens, go back to ODBC Data Source Administrator where you already have the Google BigQuery ODBC data source created and configured, and follow these steps on how to Import data source configuration into the Gateway:

   • Open ODBC data source configuration and click Copy settings:
     

     ZappySys API Driver - Configuration [Version: 2.0.1.10418]

     ZappySys API Driver - Google BigQuery

     Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required.

     GoogleBigqueryDSN

     
   • The window opens, telling us the connection string was successfully copied to the clipboard:

   • Then go to Data Gateway configuration and in data source configuration window click Load settings:

     GoogleBigqueryDSN

     

     ZappySys API Driver - Configuration [Version: 2.0.1.10418]

     ZappySys API Driver - Google BigQuery

     Read and write Google BigQuery data effortlessly. Query, integrate, and manage datasets, tables, and jobs — almost no coding required.

     GoogleBigqueryDSN

     
   • Once a window opens, just paste the settings by pressing CTRL+V or by clicking right mouse button and then Paste option.

5. Once done, go to the Network Settings tab and Add a firewall rule for inbound traffic:

   
   • This will initially allow all inbound traffic.
   • Click Edit IP filters to restrict access to specific IP addresses or ranges.

6. Crucial Step: After creating or modifying the data source, you must:

   • Click the Save button to persist your changes.
   • Hit Yes when prompted to restart the Data Gateway service.

   This ensures all changes are properly applied:

   
   Skipping this step may cause the new settings to fail, preventing you from connecting to the data source.

Create ODBC data source to connect to the gateway

In this part we will create an ODBC data source to connect to the ZappySys Data Gateway from PowerShell. To achieve that, let's perform these steps:

1. Search for odbc and open the ODBC Data Sources (64-bit):

   

2. Create a User data source (User DSN) based on the ODBC Driver 17 for SQL Server driver:

   ODBC Driver 17 for SQL Server

   
   If you don't see the ODBC Driver 17 for SQL Server driver in the list, choose a similar version.

3. Then set a Name for the data source (e.g. Gateway) and the address of the Data Gateway:

   ZappySysGatewayDSN

   localhost,5000

   
   Make sure you separate the hostname and port with a comma, e.g. localhost,5000.

4. Proceed with the authentication part:

   • Select SQL Server authentication
   • In the Login ID field enter the user name you created in the Data Gateway, e.g., john
   • Set Password to the one you configured in the Data Gateway
   

5. Then set the default database property to GoogleBigqueryDSN (the one we used in the Data Gateway):

   GoogleBigqueryDSN

   GoogleBigqueryDSN

   
   Make sure to type the data source name manually or copy/paste it directly into the field. Using the dropdown might fail because the Trust server certificate option is not enabled yet (next step).

6. Continue by checking the Trust server certificate option:

   

7. Once you do that, test the connection:

   

8. If the connection is successful, everything is good:

   

9. Done!

We are ready to move to the final step. Let's do it!

Access data in PowerShell via the gateway

Finally, we are ready to read data from Google BigQuery in PowerShell via the Data Gateway. Follow these final steps:

1. Go back to PowerShell.

2. Use this code snippet to read the data using ZappySysGatewayDSN data source:

   "DSN=ZappySysGatewayDSN"

   

   For your convenience, here is the whole PowerShell script:

   # Configure connection string and query
   $connectionString = "DSN=ZappySysGatewayDSN"
   $query = "SELECT * FROM Customers"
   
   # Instantiate OdbcDataAdapter and DataTable
   $adapter = New-Object System.Data.Odbc.OdbcDataAdapter($query, $connectionString)
   $table = New-Object System.Data.DataTable
   
   # Fill the table with data
   $adapter.Fill($table)
   
   # Since we know we will be reading just 4 columns, let's define format for those 4 columns, each separated by a tab
   $format = "{0}`t{1}`t{2}`t{3}"
   
   # Display data in the console
   foreach ($row in $table.Rows)
   {
       # Construct line based on the format and individual Google BigQuery fields
       $line = $format -f ($row["CustomerId"], $row["CompanyName"], $row["Country"], $row["Phone"])
       Write-Host $line
   }
   

   Access specific Google BigQuery table field using this code snippet:

   $field = $row["ColumnName"]

   You will find more info on how to manipulate DataTable.Rows property in Microsoft .NET reference.
   For demonstration purposes we are using sample tables which may not be available in Google BigQuery.

3. Read the data the same way we discussed at the beginning of this article.

4. That's it!

Now you can connect to Google BigQuery data in PowerShell via the Data Gateway.

Supported Google BigQuery Connector actions

Got a specific use case in mind? We've mapped out exactly how to perform a variety of essential Google BigQuery operations directly in PowerShell, so you don't have to figure out the setup from scratch. Check out the step-by-step guides below:

• [Dynamic Endpoint]
• Create Dataset
• Delete Dataset
• Delete Table
• Get Query Schema (From SQL)
• Get Table Schema
• Insert Table Data
• List Datasets
• List Projects
• List Tables
• Post Dynamic Endpoint
• Read Data using SQL Query -OR- Execute Script (i.e. CREATE, SELECT, INSERT, UPDATE, DELETE)
• Read Table Rows
• Make Generic REST API Request
• Make Generic REST API Request (Bulk Write)

Conclusion

In this article we showed you how to connect to Google BigQuery in PowerShell and integrate data without writing complex code — all of this was powered by Google BigQuery ODBC Driver.

Download ODBC PowerPack now or ping us via chat if you have any questions or are looking for a specific feature (you can also reach out to us by submitting a ticket):

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