VMFocus

This blog post is a continuation of Part 1 Using Azure Data Factory to Copy Data Between Azure File Shares and
Part 2 Using Azure Data Factory to Copy Data Between Azure File Shares. In this final part we are going to configure alerts to send an email on a failed pipeline run.

First of all select your Data Factory and then Select > Alerts > New Alerts Rule

In the previous configuration, the Azure Data Factory is running once a day. So with this in mind, we are going to Select ‘Add Condition’ then Failed Pipeline Runs.

Scroll down and Select Alert Logic. Ensure the conditions are set to Greater Than, Total 1. This essentially defines that if an issue occurs, perform an action.

Under the Evaluation based on, Select 12 Hours and Frequency Every Hour. This is how often the query is evaluated. It should look something like this:

Next we need to create an Action Group so when the above condition is met, an action is taken. I have called my Action Group VMF-WE-DFAG01, which stands for VMFocus, West Europe, DataFactory, ActionGroup 01.

For the short name, I have used Copy Failure, note this needs to be under 12 characters long.

Finally, I have chosen the ‘Action Type’ as Email/SMS/Push and entered in the appropriate contact details. Once done it should look something like this.

After a short while, you will receive an email from Microsoft Azure to confirm that you have been added to an Action Group.

Finally we want to give the Alert Rule a Name and a Description, such as the below.

That’s it your Azure Data Factory is all configured and ready for production use!

This blog post is a continuation of Part 1 Using Azure Data Factory to Copy Data Between Azure File Shares. So lets get cracking with the storage account configuration.

Storage Account Configuration

Lets start off with the basics, we will have two storage accounts which are:

• vmfwepsts001 which is the source datastore
• vmfwedsts001 which is the sink datastore

Within each storage account we have three file shares:

• documents
• images
• videos

When configured each storage account should look something like this.

Right lets move onto the Data Factory configuration.

Data Factory Configuration

I have created a V2 Data Factory called vmfwepdf001.  Next let’s click on Author & Monitor as shown below.

This will now redirect us to the Azure Data Factory landing page.  We need to select ‘Copy Data’.

We need to give the pipeline a name, in this instance, I have chosen Document Share Copy.  To keep the file shares in ‘sync’ we are going to use a schedule with a trigger type of ‘schedule’.

Depending on how often you want the pipeline to run, you can run the task every minute if required with no end date.  I have chosen a daily basis as shown in the screenshot below.

When your ready, click next.  We are now ready to select our Source Data Storage which will be ‘Azure File Storage’.  To enable Azure Data Factory to access the Storage Account we need to Create a New Connection.

A new Linked Service, popup box will appear, ensure you select Azure File Storage.  Give the Linked Service a name, I have used ‘ProductionDocuments’. You can create a custom Integration Runtime to allow the data processing to occur in a specific Azure Region if required.  In this instance, I’m going to leave it as ‘AutoResolveIntegrationRuntime’.

Azure Data Factory requires the Host to be in a specific format which is //storageaccountname.file.core.windows.net/filesharename

The user name is your storage account name and the password is your storage account access key.

The below screenshot provides the configuration.

If you have entered everything correct;y, when you click on ‘Test Connection’ you should receive a Green Tick! Click Next and then Next again, it will test your connection again.

When you are greeted with the ”input file or folder’ screen, we need to define a few pieces of information as follows:

• File or Folder – leave this blank unless you want to focus on a specific file or sub-folder
• File Loading Behaviour – this is really a design decision between load all files and incremental load : LastModifiedDate
• Copy File Recursively – Copy all files and subfolders, I would suggest selecting this
• Compression Type – None

Once configured it should look something like this:

Follow the same process for the Destination Data Store, when you get to the output file or folder screen, we need to define a few settings as follows:

• File or Folder – leave this blank unless you want to focus on a specific file or sub-folder
• Compression Type – None
• Copy Behaviour – Preserve hierarchy which means we will preserve the folder structure

Once configured it should look like this:

Click next then Next and you will see a Summary of your configuration.  Click Next and you should see your Data Factory completed.

Does It Work?

Lets check that this works.  I have loaded a few files into my Production Storage Account under Documents.

On the Azure Data Factory Landing page, click the Pencil (top left) > Select Pipelines > Document Share Copy > Trigger > Trigger Now as per the screenshot below.

Checking my Development Storage Account, I now have the three files available, success!

I hope you found this post useful, tune in for some more in the near future.

I was set an interesting challenge by a customer to copy the data in their Production Subscription Azure File Shares into their Development Subscription Azure File Shares. The reason behind this was to ensure that any uploads to their Production environment are kept inline with the Development environment, enabling testing to be performed on ‘live’ data.

The customer wanted something which was easy to manage, which provided visibility of data movement tasks within the Azure Portal without needing to manage and maintain PowerShell scripts.

The answer to this was Azure Data Factory.

What Is Azure Data Factory?

Azure Data Factory is a managed data integration service that enables data driven workflows between either on-premises to public cloud or within public clouds.

Pipelines

A pipeline is a logical grouping of activities that together perform a task.  The activities within the pipeline define actions to perform on data. 

Data Factory supports three types of activities data movement activities, data transformation activities and control activities. In this use case, data movement activities will be used to copy data from the source data store to the destination data sink.

Linked Service

Linked Services are used to link data stores to the Azure Data Factory.   With the ‘data set’ representing the structure of the data and the linked service defining the connection to the external data source. The diagram below provides a logical overview of this.

Integration Runtime

For copy activities an integration runtime is required to determine the source and sink linked services to define the direction of data flow.  To ensure data locality a custom integration runtime will be used within West Europe.

Source Datastore

Each file share within the vmfwepsts001 Storage Account is an individual linked service.  Therefore, four source linked services will be defined for data, documents, images and videos.

Sink Datastore

Each destination file share within the vmfwedsts001 Storage Account is an individual linked service.  Therefore, four source linked services will be defined for data, documents, images and videos.

Copy behaviour to the sink datastore can be undertaken using three methods:

• Preserve Hierarchy the relative path of source file to source folder is identical to the relative path of the target file and folder
• Flatten Hierarchy all files from the source folder are in the first level of target folder.  The target files have auto generated names
• Merge Files merges all files from the source folder to one file, using an auto generated name

To maintain the file and folder structure, preserve hierarchy copy behaviour will be used.

Tune in for the next blog post when we will cover the configuration settings.