Google BigQuery Connector for PowerShell
In this article you will learn how to integrate Using Google BigQuery Connector you will be able to connect, read, and write data from within PowerShell. Follow the steps below to see how we would accomplish that. The driver mentioned above is part of ODBC PowerPack which is a collection of high-performance Drivers for various API data source (i.e. REST API, JSON, XML, CSV, Amazon S3 and many more). Using familiar SQL query language you can make live connections and read/write data from API sources or JSON / XML / CSV Files inside SQL Server (T-SQL) or your favorite Reporting (i.e. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i.e. Informatica, Talend, Pentaho, SSIS). You can also call our drivers from programming languages such as JAVA, C#, Python, PowerShell etc. If you are new to ODBC and ZappySys ODBC PowerPack then check the following links to get started. |
Connect to Google BigQuery in other apps
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Create ODBC Data Source (DSN) based on ZappySys API Driver
Step-by-step instructions
To get data from Google BigQuery using PowerShell 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 PowerShell. Perform these steps:
-
Install ZappySys ODBC PowerPack.
-
Open ODBC Data Sources (x64):
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Create a User Data Source (User DSN) based on ZappySys API Driver
ZappySys API DriverYou 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. -
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:
GoogleBigQueryDSNGoogle BigQuery -
Now it's time to configure the Connection Manager. Select Authentication Type, e.g. Token Authentication. Then select API Base URL (in most cases, the default one is the right one). More info is available in the Authentication section.
Steps to get Google BigQuery Credentials
This connection can be configured using two ways. Use Default App (Created by ZappySys) OR Use Custom App created by you.
To use minimum settings you can start with ZappySys created App. Just change UseCustomApp=false on the properties grid so you dont need ClientID / Secret. When you click Generate Token you might see warning about App is not trusted (Simply Click Advanced Link to expand hidden section and then click Go to App link to Proceed). To register custom App, perform the following steps (Detailed steps found in the help link at the end)- Go to Google API Console
- From the Project Dropdown (usually found at the top bar) click Select Project
- On Project Propup click CREATE PROJECT
- Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
- Click ENABLE APIS AND SERVICES
- Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
- Search BigQuery API. Select and click ENABLE
- Search Cloud Resource Manager API. Select and click ENABLE
- Go to back to main screen of Google API Console
Click OAuth consent screen Tab. Enter necessary details and Save.
- Choose Testing as Publishing status
- Set application User type to Internal, if possible
- If MAKE INTERNAL option is disabled, then add a user in Test users section, which you will use in authentication process when generating Access and Refresh tokens
- Click Credentials Tab
- Click CREATE CREDENTIALS (some where in topbar) and select OAuth Client ID option.
- When prompted Select Application Type as Desktop App and click Create to receive your ClientID and Secret. Later on you can use this information now to configure Connection with UseCustomApp=true.
- Go to OAuth Consent Screen tab. Under Publishing Status click PUBLISH APP to ensure your refresh token doesnt expire often. If you planning to use App for Private use then do not have to worry about Verification Status after Publish.
Fill in all required parameters and set optional parameters if needed:
GoogleBigQueryDSNGoogle BigQueryUser Account [OAuth]https://www.googleapis.com/bigquery/v2Required 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... Redirect URL (Only for Web App) Fill in the parameter... Steps to get Google BigQuery Credentials
Use these steps to authenticate as service account rather than Google / GSuite User. Learn more about service account here Basically to call Google API as Service account we need to perform following steps listed in 3 sections (Detailed steps found in the help link at the end)Create Project
First thing is create a Project so we can call Google API. Skip this section if you already have Project (Go to next section)- Go to Google API Console
- From the Project Dropdown (usually found at the top bar) click Select Project
- On Project Propup click CREATE PROJECT
- Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
- Click ENABLE APIS AND SERVICES
- Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
- Search BigQuery API. Select and click ENABLE
- Search Cloud Resource Manager API. Select and click ENABLE
Create Service Account
Once Project is created and APIs are enabled we can now create a service account under that project. Service account has its ID which looks like some email ID (not to confuse with Google /Gmail email ID)- Go to Create Service Account
- From the Project Dropdown (usually found at the top bar) click Select Project
- Enter Service account name and Service account description
- Click on Create. Now you should see an option to assign Service Account permissions (See Next Section).
Give Permission to Service Account
By default service account cant access BigQuery data or List BigQuery Projects so we need to give that permission using below steps.- After you Create Service Account look for Permission drop down in the Wizard.
- Choose BigQuery -> BigQuery Admin role so we can read/write data. (NOTE: If you just need read only access then you can choose BigQuery Data Viewer)
- Now choose one more Project -> Viewer and add that role so we can query Project Ids.
- Click on Continue. Now you should see an option to Create Key (See Next Section).
Create Key (P12)
Once service account is created and Permission is assigned we need to create key file.- In the Cloud Console, click the email address for the service account that you created.
- Click Keys.
- Click Add key, then click Create new key.
- Click Create and select P12 format. A P12 key file is downloaded to your computer. We will use this file in our API connection.
- Click Close.
- Now you may use downloaded *.p12 key file as secret file and Service Account Email as Client ID (e.g. some_name@some_name.iam.gserviceaccount.com).
Manage Permissions / Give Access to Other Projects
We saw how to add permissions for Service Account during Account Creation Wizard but if you ever wish to edit after its created or you wish to give permission for other projects then perform forllowing steps.- From the top Select Project for which you like to edit Permission.
- Go to IAM Menu option (here)
Link to IAM: https://console.cloud.google.com/iam-admin/iam - Goto Permissions tab. Over there you will find ADD button.
- Enter Service account email for which you like to grant permission. Select role you wish to assign.
Fill in all required parameters and set optional parameters if needed:
GoogleBigQueryDSNGoogle BigQueryService Account (Using Private Key File) [OAuth]https://www.googleapis.com/bigquery/v2Required Parameters Service Account Email Fill in the parameter... P12 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... -
Once the data source has been configured, you can preview data. Select the Preview tab and use settings similar to the following to preview data:
-
Click OK to finish creating the data source.
Video instructions
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:
Viewing data in a terminal- 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
- Open your favorite PowerShell IDE (we are using Visual Studio Code).
-
Then simply follow these instructions:
"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 $line }
Access specific Google BigQuery table field using this code snippet:
You will find more info on how to manipulate$field = $row["ColumnName"]
DataTable.Rows
property in Microsoft .NET reference.For demonstration purposes we are using sample tables which may not be available in Google BigQuery. -
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:
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
}
LIMIT
keyword in the query, e.g.:
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:
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Open ODBC data source configuration and click Copy settings:
ZappySys API Driver - Google BigQueryRead / write Google BigQuery data inside your app without coding using easy to use high performance API ConnectorGoogleBigQueryDSN
- The window opens, telling us the connection string was successfully copied to the clipboard:
- Then just paste the connection string into your script:
- You are good to go! The script will execute the same way as using a DSN.
Have in mind that using 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. It usually happens when API provider generates a very long Refresh Token when OAuth is at play. If it happens you use such API, you may get this error:
"Connection string exceeds maximum allowed length of 1024"
But there is a solution to this by storing the Refresh Token in a file instead of directly in ODBC connection string. Follow the steps below to achieve this.
- Open your ODBC data source
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Hit Advanced tab and enter a file path:
ZappySys API Driver - Google BigQueryRead / write Google BigQuery data inside your app without coding using easy to use high performance API ConnectorGoogleBigQueryDSNIf you cannot find the Advanced tab, try looking for Click to Configure link in Properties tab, under URL text box. Click it, and then configure the file path in OAuth Grant Options tab.
- Then click Copy settings button to copy a full connection string and paste it into your script.
- Proceed by pasting the connection string into your C# code as an argument when calling the OdbcConnection object's constructor.
- Before using it we still have to shorten it manually like this:
- Our troubles are over, now you should be able to execute the script 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
ConvertTo-Csv
, ConvertTo-Json
, and ConvertTo-Html
for other data manipulation scenarios.
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
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Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
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Enter the desired Procedure name and click on OK:
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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>';
-
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';
-
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''')
-
Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
Create Custom Virtual Table in ZappySys Driver
ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.
If you're dealing with Microsoft Access and need to import data from an SQL query, it's important to note that Access doesn't allow direct import of SQL queries. Instead, you can create custom objects (Virtual Tables) to handle the import process.
Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.
-
Go to Custom Objects Tab and Click on Add button and Select Add Table:
-
Enter the desired Table name and click on OK:
-
And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.
-
Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
SELECT "ShipCountry", "OrderID", "CustomerID", "EmployeeID", "OrderDate", "RequiredDate", "ShippedDate", "ShipVia", "Freight", "ShipName", "ShipAddress", "ShipCity", "ShipRegion", "ShipPostalCode" FROM "Orders" Where "ShipCountry"='USA'
-
That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
SELECT * FROM "vt__usa_orders_only"
-
Let's generate the SQL Server Query Code to make the API call using 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''')
-
Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
Actions supported by Google BigQuery Connector
Google BigQuery Connector 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.Parameter | Description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Use Legacy SQL Syntax? |
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timeout (Milliseconds) |
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Job Location |
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ProjectId |
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DatasetId |
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TableId |
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SearchFilter |
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ProjectId |
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SearchFilter |
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all |
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ProjectId |
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Dataset Name |
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ProjectId |
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DatasetId |
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Delete All Tables |
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ProjectId |
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DatasetId |
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TableId |
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ProjectId |
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DatasetId |
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Filter |
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timeout (Milliseconds) |
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Job Location |
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DatasetId |
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TableId |
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Filter |
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ProjectId |
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DatasetId |
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TableId |
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Url |
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Body |
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IsMultiPart |
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Filter |
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Headers |
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Google BigQuery Connector Examples for PowerShell 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
#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 PowerShell and integrate data without any coding. Click here to Download Google BigQuery Connector for PowerShell 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 PowerShell
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Download Google BigQuery Connector for PowerShell
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Integration Google BigQuery in PowerShell
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Consume real-time Google BigQuery API data in PowerShell
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 PowerShell
Load Google BigQuery in PowerShell
Load Google BigQuery data in PowerShell
Read Google BigQuery data in PowerShell
Google BigQuery API Call in PowerShell