Azure Data Factory (ADF) Google BigQuery Connector
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 Azure Data Factory (ADF). Follow the steps below to see how we would accomplish that. Driver mentioned in this article is part of ODBC PowerPack which is a collection of high-performance Drivers for various API data source (i.e. REST API, JSON, XML, CSV, Amazon S3 and many more). Using familiar SQL query language you can make live connections and read/write data from API sources or JSON / XML / CSV Files inside SQL Server (T-SQL) or your favorite Reporting (i.e. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i.e. Informatica, Talend, Pentaho, SSIS). You can also call our drivers from programming languages such as JAVA, C#, Python, PowerShell etc. If you are new to ODBC and ZappySys ODBC PowerPack then check the following links to get started. |
See also
|
Create ODBC Data Source (DSN) based on ZappySys API Driver
To get data from GoogleBigQuery using Azure Data Factory (ADF) we first need to create a DSN (Data Source) which will access data from GoogleBigQuery. We will later be able to read data using Azure Data Factory (ADF). Perform these steps:
-
Install ZappySys ODBC PowerPack.
-
Open ODBC Data Sources (x64):
-
Create a System Data Source (System 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. Furthermore, a User DSN may be created instead, but then you will not be able to use the connection from Windows Services (or any application running under a Windows System Account). -
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. You can use this information now to configure Connection with UseCustomApp=true.
Fill in all required parameters and set optional parameters if needed:
GoogleBigQueryDSNUser Account [OAuth]https://www.googleapis.com/bigquery/v2Required Parameters UseCustomApp Fill in the parameter... ProjectId Fill in the parameter... DatasetId Fill in the parameter... Optional Parameters ClientId Fill in the parameter... ClientSecret Fill in the parameter... Scope Fill in the parameter... RetryMode Fill in the parameter... RetryStatusCodeList Fill in the parameter... RetryCountMax Fill in the parameter... RetryMultiplyWaitTime Fill in the parameter... Job Location Fill in the parameter... 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:
GoogleBigQueryDSNService Account (Using Private Key File) [OAuth]https://www.googleapis.com/bigquery/v2Required Parameters Service Account Email Fill in the parameter... Service Account Private Key Path (i.e. *.p12) Fill in the parameter... ProjectId Fill in the parameter... DatasetId Fill in the parameter... Optional Parameters Scope Fill in the parameter... RetryMode Fill in the parameter... RetryStatusCodeList Fill in the parameter... RetryCountMax Fill in the parameter... RetryMultiplyWaitTime Fill in the parameter... Job Location Fill in the parameter... -
Once the data source has been configured, you can preview data. Select the Preview tab and use settings similar to the following to preview data:
-
Click OK to finish creating the data source.
Read data in Azure Data Factory (ADF) from ODBC datasource (Google BigQuery)
-
To start press New button:
-
Select "Azure, Self-Hosted" option:
-
Select "Self-Hosted" option:
-
Set a name, we will use "OnPremisesRuntime":
-
Download and install Microsoft Integration Runtime.
-
Launch Integration Runtime and copy/paste Authentication Key from Integration Runtime configuration in Azure Portal:
-
After finishing registering the Integration Runtime node, you should see a similar view:
-
Go back to Azure Portal and finish adding new Integration Runtime. You should see it was successfully added:
-
Go to Linked services section and create a new Linked service based on ODBC:
-
Select "ODBC" service:
-
Configure new ODBC service. Use the same DSN name we used in the previous step and copy it to Connection string box:
GoogleBigQueryDSNDSN=GoogleBigQueryDSN -
For created ODBC service create ODBC-based dataset:
-
Go to your pipeline and add Copy data connector into the flow. In Source section use OdbcDataset we created as a source dataset:
-
Then go to Sink section and select a destination/sink dataset. In this example we use precreated AzureBlobStorageDataset which saves data into an Azure Blob:
-
Finally, run the pipeline and see data being transferred from OdbcDataset to your destination dataset:
Create Custom Store Procedure in ZappySys Driver
You can create procedures to encapsulate custom logic and then only pass handful parameters rather than long SQL to execute your API call.
Steps to create Custom Store Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
-
Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
-
Enter the desired Procedure name and click on OK:
-
Select the created Store Procedure and write the your desired store procedure and Save it and it will create the custom store procedure in the ZappySys Driver:
Here is an example stored procedure for ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
CREATE PROCEDURE [usp_get_orders] @fromdate = '<<yyyy-MM-dd,FUN_TODAY>>' AS SELECT * FROM Orders where OrderDate >= '<@fromdate>';
-
That's it now go to Preview Tab and Execute your Store Procedure using Exec Command. In this example it will extract the orders from the date 1996-01-01:
Exec usp_get_orders '1996-01-01';
Create Custom Virtual Table in ZappySys Driver
ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.
Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.
-
Go to Custom Objects Tab and Click on Add button and Select Add Table:
-
Enter the desired Table name and click on OK:
-
And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.
-
Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
SELECT "ShipCountry", "OrderID", "CustomerID", "EmployeeID", "OrderDate", "RequiredDate", "ShippedDate", "ShipVia", "Freight", "ShipName", "ShipAddress", "ShipCity", "ShipRegion", "ShipPostalCode" FROM "Orders" Where "ShipCountry"='USA'
-
That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
SELECT * FROM "vt__usa_orders_only"
Conclusion
In this article we discussed how to connect to Google BigQuery in Azure Data Factory (ADF) and integrate data without any coding. Click here to Download Google BigQuery Connector for Azure Data Factory (ADF) 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 Azure Data Factory (ADF)
Documentation
Actions supported by Google BigQuery Connector
Google BigQuery Connector support following actions for REST API integration. If some actions are not listed below then you can easily edit Connector file and enhance out of the box functionality.Parameter | Description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SQL Statement (i.e. SELECT / DROP / CREATE) |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Use Legacy SQL Syntax? |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
timeout (Milliseconds) |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Job Location |
|
Parameter | Description |
---|---|
ProjectId |
|
DatasetId |
|
TableId |
|
Parameter | Description |
---|
Parameter | Description |
---|---|
SearchFilter |
|
Parameter | Description | ||||||
---|---|---|---|---|---|---|---|
ProjectId |
|
||||||
SearchFilter |
|
||||||
all |
|
Parameter | Description |
---|---|
ProjectId |
|
Dataset Name |
|
Description |
|
Parameter | Description | ||||||
---|---|---|---|---|---|---|---|
ProjectId |
|
||||||
DatasetId |
|
||||||
Delete All Tables |
|
Parameter | Description |
---|---|
ProjectId |
|
DatasetId |
|
TableId |
|
Parameter | Description |
---|---|
ProjectId |
|
DatasetId |
|
Parameter | Description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SQL Query |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Use Legacy SQL Syntax? |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
timeout (Milliseconds) |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Job Location |
|
Parameter | Description |
---|---|
DatasetId |
|
TableId |
|
Parameter | Description |
---|---|
ProjectId |
|
DatasetId |
|
TableId |
|
Parameter | Description |
---|---|
Url |
|
Body |
|
IsMultiPart |
|
Filter |
|
Headers |
|
Other App Integration scenarios for Google BigQuery
Other Connectors for Azure Data Factory (ADF)
Download Google BigQuery Connector for Azure Data Factory (ADF)
Documentation
How to connect Google BigQuery in Azure Data Factory (ADF)?
How to get Google BigQuery data in Azure Data Factory (ADF)?
How to read Google BigQuery data in Azure Data Factory (ADF)?
How to load Google BigQuery data in Azure Data Factory (ADF)?
How to import Google BigQuery data in Azure Data Factory (ADF)?
How to pull Google BigQuery data in Azure Data Factory (ADF)?
How to push data to Google BigQuery in Azure Data Factory (ADF)?
How to write data to Google BigQuery in Azure Data Factory (ADF)?
How to POST data to Google BigQuery in Azure Data Factory (ADF)?
Call Google BigQuery API in Azure Data Factory (ADF)
Consume Google BigQuery API in Azure Data Factory (ADF)
Google BigQuery Azure Data Factory (ADF) Automate
Google BigQuery Azure Data Factory (ADF) Integration
Integration Google BigQuery in Azure Data Factory (ADF)
Consume real-time Google BigQuery data in Azure Data Factory (ADF)
Consume realtime Google BigQuery API data in Azure Data Factory (ADF)
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 Azure Data Factory (ADF)
Load Google BigQuery in Azure Data Factory (ADF)
Load Google BigQuery data in Azure Data Factory (ADF)
Read Google BigQuery data in Azure Data Factory (ADF)
Google BigQuery API Call in Azure Data Factory (ADF)