Google BigQuery Connector for Azure Data Factory (SSIS)

Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector
You can use this connector to integrate Google BigQuery data inside SSIS and SQL Server. Let's take a look at the steps below to see how exactly to accomplish that.

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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:

  1. Download SSIS PowerPack trial installer.
    Make sure you don't rename the installer and keep it named as SSISPowerPackSetup_64bit_Trial.msi.
  2. Create a text file and name it main.cmd (make it all lowercase, very important).
  3. 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 complete
    This is the entry-point script that is executed when Azure-SSIS runtime is started.
  4. 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:

  1. Download SSIS PowerPack paid installer.
    Make sure you don't rename the installer and keep it named as SSISPowerPackSetup_64bit.msi.
  2. Have your SSIS PowerPack license key handy, we will need it in the below script.
  3. Create a text file and name it main.cmd (make it all lowercase, very important).
  4. Copy and paste the below script into it.
  5. Paste your license key by replacing parameter's --register argument with your real license key.
  6. 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 complete
    This is the entry-point script that is executed when Azure-SSIS runtime is started.
  7. 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:

  1. 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.
  2. Find Blob Containers node, right-click on it and hit Create Blob Container option: Create a new blob container in Azure Storage Explorer
  3. Upload the two custom setup files — main.cmd & the MSI installer — into your Azure Blob Storage container's folder: Upload SSIS Custom Setup Files to Azure Data Factory
  4. 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:

  1. Install and launch Azure Storage Explorer.
  2. Right-click on the Storage Accounts node and then hit Connect to Azure storage... menu item: Add Azure Storage account to Azure Storage Explorer
  3. Proceed by right-clicking on that container node and select Get Shared Access Signature... option.
  4. 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.
  5. Select Read, Create, Write, and List permissions: Generate SAS URI in Azure Storage Explorer for Azure Data Factory Custom Setup
    We also recommend to add Delete permission too to support future functionality.
  6. Copy SAS URL to the clipboard and save it for the next step: Get container SAS URI for Azure Data Factory SSIS Custom Setup You can also generate and copy SAS URL from within Azure Portal itself: Generate SAS URI in Azure Data Factory Custom Setup via online portal

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:

  1. Firstly, perform the steps described in Create an Azure-SSIS integration runtime article in Azure Data Factory reference.
  2. In Advanced settings page section, configure Custom setup container SAS URI you obtained in the previous step: Configure SAS URI in Azure Data Factory custom setup
  3. And you are done! That was quick! You can see your Azure-SSIS runtime up and running: Verify Azure-SSIS runtime status in Azure Data Factory portal

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! Deploy SSIS package to Azure Data Factory from Visual Studio

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):

  1. Connect to the SQL Server which is linked to your Azure-SSIS runtime and contains SSISDB database.
  2. Navigate to Integration Services Catalog » Your Folder » Your Project » Your Package, right-click on it, and hit Execute...: Execute SSIS package using SQL Server Management Studio (SSMS)
  3. 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: Monitor SSIS package execution using SSMS UI

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:

  1. 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
  2. Start Azure-SSIS runtime.
    This will unregister your license on the original Azure-SSIS runtime.
  3. Stop Azure-SSIS runtime to deallocate resources in Azure.
  4. 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)
Option Value
Example1 SELECT title,id,language,wp_namespace,reversion_id ,comment,num_characters FROM bigquery-public-data.samples.wikipedia LIMIT 1000
Example2 CREATE TABLE TestDataset.Table1 (ID INT64,Name STRING,BirthDate DATETIME, Active BOOL)
Example3 INSERT TestDataset.Table1 (ID, Name,BirthDate,Active) VALUES(1,'AA','2020-01-01',true),(2,'BB','2020-01-02',true),(3,'CC','2020-01-03',false)
Use Legacy SQL Syntax?
Option Value
false false
true true
timeout (Milliseconds) Wait until timeout is reached.
Option Value
false false
true true
Job Location The geographic location where the job should run. For Non-EU and Non-US datacenters we suggest you to supply this parameter to avoid any error.
Option Value
System Default
Data centers in the United States US
Data centers in the European Union EU
Columbus, Ohio us-east5
Iowa us-central1
Las Vegas us-west4
Los Angeles us-west2
Montréal northamerica-northeast1
Northern Virginia us-east4
Oregon us-west1
Salt Lake City us-west3
São Paulo southamerica-east1
Santiago southamerica-west1
South Carolina us-east1
Toronto northamerica-northeast2
Delhi asia-south2
Hong Kong asia-east2
Jakarta asia-southeast2
Melbourne australia-southeast2
Mumbai asia-south1
Osaka asia-northeast2
Seoul asia-northeast3
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1
Belgium europe-west1
Finland europe-north1
Frankfurt europe-west3
London europe-west2
Madrid europe-southwest1
Milan europe-west8
Netherlands europe-west4
Paris europe-west9
Warsaw europe-central2
Zürich europe-west6
AWS - US East (N. Virginia) aws-us-east-1
Azure - East US 2 azure-eastus2
Custom Name (Type your own) type-region-id-here
 Read Table Rows
Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.    [Read more...]
Parameter Description
ProjectId Leave this value blank to use ProjectId from connection settings
DatasetId Leave this value blank to use DatasetId from connection settings
TableId
 [$parent.tableReference.datasetId$].[$parent.tableReference.tableId$]
Read data from [$parent.tableReference.datasetId$].[$parent.tableReference.tableId$] for project .    [Read more...]
Parameter Description
 List Projects
Lists Projects that the caller has permission on and satisfy the specified filter.    [Read more...]
Parameter Description
SearchFilter An expression for filtering the results of the request. Filter rules are case insensitive. If multiple fields are included in a filter query, the query will return results that match any of the fields. Some eligible fields for filtering are: name, id, labels.{key} (where key is the name of a label), parent.type, parent.id, lifecycleState. Example: name:how*
 List Datasets
Lists all BigQuery datasets in the specified project to which the user has been granted the READER dataset role.    [Read more...]
Parameter Description
ProjectId
SearchFilter An expression for filtering the results of the request. Filter rules are case insensitive. If multiple fields are included in a filter query, the query will return results that match any of the fields. Some eligible fields for filtering are: name, id, labels.{key} (where key is the name of a label), parent.type, parent.id, lifecycleState. Example: name:how*
all Whether to list all datasets, including hidden ones
Option Value
True True
False False
 Create Dataset
Creates a new empty dataset.    [Read more...]
Parameter Description
ProjectId
Dataset Name Enter dataset name
Description
 Delete Dataset
Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name.    [Read more...]
Parameter Description
ProjectId
DatasetId
Delete All Tables If True, delete all the tables in the dataset. If False and the dataset contains tables, the request will fail. Default is False
Option Value
True True
False False
 Delete Table
Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name.    [Read more...]
Parameter Description
ProjectId
DatasetId
TableId
 List Tables
Lists BigQuery Tables for the specified project / dataset to which the user has been granted the READER dataset role.    [Read more...]
Parameter Description
ProjectId
DatasetId
 Get Query Schema (From SQL)
Runs a BigQuery SQL query synchronously and returns query schema    [Read more...]
Parameter Description
SQL Query
Filter
Use Legacy SQL Syntax?
Option Value
false false
true true
timeout (Milliseconds) Wait until timeout is reached.
Option Value
false false
true true
Job Location The geographic location where the job should run. For Non-EU and Non-US datacenters we suggest you to supply this parameter to avoid any error.
Option Value
System Default
Data centers in the United States US
Data centers in the European Union EU
Columbus, Ohio us-east5
Iowa us-central1
Las Vegas us-west4
Los Angeles us-west2
Montréal northamerica-northeast1
Northern Virginia us-east4
Oregon us-west1
Salt Lake City us-west3
São Paulo southamerica-east1
Santiago southamerica-west1
South Carolina us-east1
Toronto northamerica-northeast2
Delhi asia-south2
Hong Kong asia-east2
Jakarta asia-southeast2
Melbourne australia-southeast2
Mumbai asia-south1
Osaka asia-northeast2
Seoul asia-northeast3
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1
Belgium europe-west1
Finland europe-north1
Frankfurt europe-west3
London europe-west2
Madrid europe-southwest1
Milan europe-west8
Netherlands europe-west4
Paris europe-west9
Warsaw europe-central2
Zürich europe-west6
AWS - US East (N. Virginia) aws-us-east-1
Azure - East US 2 azure-eastus2
Custom Name (Type your own) type-region-id-here
 Get Table Schema
Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.    [Read more...]
Parameter Description
DatasetId
TableId
Filter
 insert_table_data
   [Read more...]
Parameter Description
ProjectId
DatasetId
TableId
 post_[$parent.tableReference.datasetId$]_[$parent.tableReference.tableId$]
   [Read more...]
 Generic Request
This is generic endpoint. Use this endpoint when some actions are not implemented by connector. Just enter partial URL (Required), Body, Method, Header etc. Most parameters are optional except URL.    [Read more...]
Parameter Description
Url API URL goes here. You can enter full URL or Partial URL relative to Base URL. If it is full URL then domain name must be part of ServiceURL or part of TrustedDomains
Body Request Body content goes here
IsMultiPart Set this option if you want to upload file(s) (i.e. POST RAW file data) or send data using Multi-Part encoding method (i.e. Content-Type: multipart/form-data). Multi-Part request allows you to mix key/value and upload files in same request. On the other hand raw upload allows only single file upload (without any key/value) ==== Raw Upload (Content-Type: application/octet-stream) ===== To upload single file in raw mode check this option and specify full file path starting with @ sign in the Body (e.g. @c:\data\myfile.zip ) ==== Form-Data / Multipart Upload (Content-Type: multipart/form-data) ===== To treat your Request data as multi part fields you must specify key/value pairs separated by new lines into RequestData field (i.e. Body). Each key value pair is entered on new-line and key/value are separated using equal sign (=). Preceding and trailing spaces are ignored also blank lines are ignored. If field value has some any special character(s) then use escape sequence (e.g. For NewLine: \r\n, For Tab: \t, For at (@): \@). When value of any field starts with at sign (@) its automatically treated as File you want to upload. By default file content type is determined based on extension however you can supply content type manually for any field using this way [ YourFileFieldName.Content-Type=some-content-type ]. By default File Upload Field always includes Content-Type in the request (non file fields do not have content-type by default unless you supply manually). For some reason if you dont want to use Content-Type header in your request then supply blank Content-Type to exclude this header altogather [e.g. SomeFieldName.Content-Type= ]. In below example we have supplied Content-Type for file2 and SomeField1, all other fields are using default content-type. See below Example of uploading multiple files along with additional fields. If some API requires you to pass Content-Type: multipart/form-data rather than multipart/form-data then manually set Request Header => Content-Type: multipart/mixed (it must starts with multipart/ else will be ignored). file1=@c:\data\Myfile1.txt file2=@c:\data\Myfile2.json file2.Content-Type=application/json SomeField1=aaaaaaa SomeField1.Content-Type=text/plain SomeField2=12345 SomeFieldWithNewLineAndTab=This is line1\r\nThis is line2\r\nThis is \ttab \ttab \ttab SomeFieldStartingWithAtSign=\@MyTwitterHandle
Filter Enter filter to extract array from response. Example: $.rows[*] --OR-- $.customers[*].orders[*]. Check your response document and find out hierarchy you like to extract
Headers Headers for Request. To enter multiple headers use double pipe or new line after each {header-name}:{value} pair

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) Documentation 

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