Create SSIS package
First of all, create an SSIS package, which will connect to Google BigQuery in SSIS. Once you do that, you are one step closer to deploying and running it in Azure-SSIS integration runtime in Azure Data Factory (ADF). Then simply proceed to the next step - creating and configuring Azure Blob Storage Container.
Prepare custom setup files for Azure-SSIS runtime
Now it's time to start preparing custom setup files for Azure-SSIS runtime. During Azure-SSIS runtime creation you can instruct ADF to perform a custom setup on a VM (Azure-SSIS node); i.e. to run the custom installer, copy files, execute PowerShell scripts, etc. In that case, your custom setup files are downloaded and run in the Azure-SSIS node (a VM) when you start the runtime. In this section we will prepare custom setup files so that you can run SSIS packages with SSIS PowerPack connectors inside in Azure-SSIS runtime.
Read more on Azure-SSIS runtime custom setup in Microsoft Azure Data Factory reference.
Trial Users
Use the step below if you are a Trial User, when you did not purchase a license key. Proceed with these steps:
-
Download SSIS PowerPack trial installer.
Make sure you don't rename the installer and keep it named as SSISPowerPackSetup_64bit_Trial.msi.
- Create a text file and name it main.cmd (make it all lowercase, very important).
-
Copy and paste this script into it and save it:
set DIR=%CUSTOM_SETUP_SCRIPT_LOG_DIR% echo Calling Step 1 : %TIME% >> "%DIR%\steps_log.txt" dir /s /b > "%DIR%\file_list.txt" echo Calling Step 2 : %TIME% >> "%DIR%\steps_log.txt" ::Install SSIS PowerPack msiexec /i "SSISPowerPackSetup_64bit_Trial.msi" ADDLOCAL=ALL /q /L*V "%DIR%\powerpack_trial_install_log.txt" echo Calling Step 3 : %TIME% >> "%DIR%\steps_log.txt" dir "C:\Program Files\Microsoft SQL Server\*Zappy*.*" /s /b >> "%DIR%\installed_files.txt" dir "C:\Program Files (x86)\Microsoft SQL Server\*Zappy*.*" /s /b >> "%DIR%\installed_files.txt" echo DONE : %TIME% >> "%DIR%\steps_log.txt" echo completeThis is the entry-point script that is executed when Azure-SSIS runtime is started. - At last! You are ready to upload these two files — main.cmd & SSISPowerPackSetup_64bit_Trial.msi — into your Azure Blob Storage container's folder, which we will do in the Upload custom setup files to Azure Blob Storage container step.
Paid Customers
Use the steps below if you are a Paid Customer, when you purchased a license. Proceed with these steps:
-
Download SSIS PowerPack paid installer.
Make sure you don't rename the installer and keep it named as SSISPowerPackSetup_64bit.msi.
- Have your SSIS PowerPack license key handy, we will need it in the below script.
- Create a text file and name it main.cmd (make it all lowercase, very important).
- Copy and paste the below script into it.
- Paste your license key by replacing parameter's
--registerargument with your real license key. -
Finally, save main.cmd:
set DIR=%CUSTOM_SETUP_SCRIPT_LOG_DIR% echo Calling Step 1 : %TIME% >> "%DIR%\steps_log.txt" dir /s /b > "%DIR%\file_list.txt" echo Calling Step 2 : %TIME% >> "%DIR%\steps_log.txt" ::Install SSIS PowerPack msiexec /i "SSISPowerPackSetup_64bit.msi" ADDLOCAL=ALL /q /L*V "%DIR%\powerpack_install_log.txt" echo Calling Step 3 : %TIME% >> "%DIR%\steps_log.txt" ::Activate PowerPack license (Optional) "C:\Program Files (x86)\ZappySys\SSIS PowerPack (64 bit)\LicenseManager.exe" -p SSISPowerPack --register "lgGAAO0-----REPLACE-WITH-YOUR-LICENSE-KEY-----czM=" --logfile "%DIR%\powerpack_register_log.txt" ::Show System Info echo Calling Step 4 : %TIME% >> "%DIR%\steps_log.txt" "C:\Program Files (x86)\ZappySys\SSIS PowerPack (64 bit)\LicenseManager.exe" -i -l "%DIR%\sysinfo_log.txt" echo Calling Step 5 : %TIME% >> "%DIR%\steps_log.txt" dir "C:\Program Files\Microsoft SQL Server\*Zappy*.*" /s /b >> "%DIR%\installed_files.txt" dir "C:\Program Files (x86)\Microsoft SQL Server\*Zappy*.*" /s /b >> "%DIR%\installed_files.txt" echo DONE : %TIME% >> "%DIR%\steps_log.txt" echo completeThis is the entry-point script that is executed when Azure-SSIS runtime is started. - At last! You are ready to upload these two files — main.cmd & SSISPowerPackSetup_64bit.msi — into your Azure Blob Storage container's folder, which we will do in the Upload custom setup files to Azure Blob Storage container step.
Upload custom setup files to Azure Blob Storage container
Within Azure Blob Storage container we will store custom setup files we prepared in the previous step so that Azure-SSIS can use them in custom setup process. Just perform these very simple, but very important steps:
-
Create Azure Blob Storage container, if you haven't done it already
Make sure you create and use Azure Blob Storage container instead of Azure Data Lake Storage folder. Azure Data Lake Storage won't allow creating an SAS URI for the container, which is a crucial step in the process.
-
Find Blob Containers node, right-click on it and hit Create Blob Container option:
-
Upload the two custom setup files — main.cmd & the MSI installer — into your Azure Blob Storage container's folder:
- It was easy, wasn't it? It's time we create an SAS URI in the next step.
Create SAS URI for Azure Blob Container
Once you have custom setup files prepared, it's time we generate an SAS URI. This SAS URI will be used by a new Azure-SSIS runtime to install SSIS PowerPack inside the runtime's node, a VM. Let's proceed together by performing the steps below:
- Install and launch Azure Storage Explorer.
-
Right-click on the Storage Accounts node and then hit Connect to Azure storage... menu item:
- Proceed by right-clicking on that container node and select Get Shared Access Signature... option.
-
Next, set the Expiry time field to a date far in the future.
If you restart Azure-SSIS runtime and your SAS URI is expired by that time, it will not start.
-
Select Read, Create, Write, and List permissions:
We also recommend to add Delete permission too to support future functionality. -
Copy SAS URL to the clipboard and save it for the next step:
You can also generate and copy SAS URL from within Azure Portal itself:
Create Azure-SSIS integration runtime
Once you have the SAS URL we obtained in the previous step, we are ready to move on to create an Azure-SSIS runtime in Azure Data Factory:
- Firstly, perform the steps described in Create an Azure-SSIS integration runtime article in Azure Data Factory reference.
-
In Advanced settings page section, configure Custom setup container SAS URI you obtained in the previous step:
-
And you are done! That was quick! You can see your Azure-SSIS runtime up and running:
The custom setup script is executed only once — at the time an Azure-SSIS runtime is started.
It is also executed if you stop and start Azure-SSIS runtime again.
Deploy SSIS package in Visual Studio
We are ready to deploy the SSIS package to Azure-SSIS runtime. Once you do that, proceed to the next step for the grand finale!
Execute SSIS package in SQL Server Management Studio (SSMS)
After all hard work, we are ready to execute SSIS package in SQL Server Management Studio (SSMS):
- Connect to the SQL Server which is linked to your Azure-SSIS runtime and contains SSISDB database.
-
Navigate to Integration Services Catalog » Your Folder » Your Project » Your Package, right-click on it, and hit Execute...:
-
To view the status of the past execution, navigate to
Integration Services Catalog » Your Folder » Your Project » Your Package, right-click on it, and select Reports » Standard Reports » All Executions menu item:
Scenarios
Moving SSIS PowerPack license to another Azure-SSIS runtime
If you are a Paid Customer, there will be a time when you no longer use Azure-SSIS runtime or you need to use your license on a different ADF instance. To transfer a license from one Azure-SSIS runtime to another, perform these steps:
-
Copy & paste this script into main.cmd we used in the previous step:
set DIR=%CUSTOM_SETUP_SCRIPT_LOG_DIR% echo Calling Step 1 : %TIME% >> "%DIR%\steps_log.txt" dir /s /b > "%DIR%\file_list.txt" echo Calling Step 2 : %TIME% >> "%DIR%\steps_log.txt" ::Install SSIS PowerPack msiexec /i "SSISPowerPackSetup_64bit.msi" ADDLOCAL=ALL /q /L*V "%DIR%\powerpack_install_log.txt" echo Calling Step 3 : %TIME% >> "%DIR%\steps_log.txt" ::De-Activate same license "C:\Program Files (x86)\ZappySys\SSIS PowerPack (64 bit)\LicenseManager.exe" -p SSISPowerPack --unregister --logfile "%DIR%\powerpack_un_register_log.txt" ::Show System Info echo Calling Step 4 : %TIME% >> "%DIR%\steps_log.txt" "C:\Program Files (x86)\ZappySys\SSIS PowerPack (64 bit)\LicenseManager.exe" -i -l "%DIR%\sysinfo_log.txt" echo Calling Step 5 : %TIME% >> "%DIR%\steps_log.txt" dir "C:\Program Files\Microsoft SQL Server\*Zappy*.*" /s /b >> "%DIR%\installed_files.txt" dir "C:\Program Files (x86)\Microsoft SQL Server\*Zappy*.*" /s /b >> "%DIR%\installed_files.txt" echo DONE : %TIME% >> "%DIR%\steps_log.txt" echo complete - Start Azure-SSIS runtime.
This will unregister your license on the original Azure-SSIS runtime.
- Stop Azure-SSIS runtime to deallocate resources in Azure.
- Now you are free to activate it on another Azure-SSIS runtime.
Advanced topics
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) |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Use Legacy SQL Syntax? |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| timeout (Milliseconds) |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Job Location |
|
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 |
|
| DatasetId |
|
| 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 |
|
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 |
|
||||||
| all |
|
Create Dataset
Creates a new empty dataset. [Read more...]
| Parameter | Description |
|---|---|
| ProjectId |
|
| 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 |
|
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? |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| timeout (Milliseconds) |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Job Location |
|
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_[$parent.tableReference.datasetId$]_[$parent.tableReference.tableId$]
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 |
|
| Body |
|
| IsMultiPart |
|
| Filter |
|
| Headers |
|
Conclusion
In this article we discussed how to connect to Google BigQuery in Azure Data Factory (SSIS) and integrate data without any coding. Click here to Download Google BigQuery Connector for Azure Data Factory (SSIS) 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 (SSIS)
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