Creating a Data Pipeline in Azure Data Factory

Create a Data Factory Instance

• Use the given Resource Group and Subscription available in the dropdown menu.
• Instance name should be unique.
• Region should be East US and Version should be V2.
• Git is not required.
• Public endpoint is required.

Create a SQL Database

• Use the given Resource Group and Subscription available in the dropdown menu.
• Database name should be unique.
• Create a new server in East US region.
• Service tier should be Basic DTU-based with .5 GB Data max size.
• Locally-redundant backup storage.
• Make sure to enable Sample data.

Create a Blob Storage

• Use the given Resource Group and Subscription available in the dropdown menu.
• Storage account name should be unique.
• Region should be East US region.
• Performance should be standard.
• Locally-redudant.
• Network access should enable public access from all networks.
• Upload the AdventureWorks Sales-2.xlsx GitHub file into a new blob container named delete.

• Create a Copy Data activity and name it SQL DB.
• The source is the SalesLT.Product table in SQL DB .
• The sink is a new table named SalesLT.Product2 in SQL DB.

• Create a Web trigger, whether the Copy Data activity succeeds or fails.
• It should trigger QuackersCrazySales.Ducks?sales=NA2.
• Create a Delete activity to delete all files in the new container in our Blob Storage account.
• This will only activate if the data was successfully copied in Objective 2.

• Create a Fail trigger to activate if the Delete activity fails.
• Enter the following Error Message, The Delete Activity Failed to Run. with the Error Code as 1Quack2.
• If the Delete activity fails then a Web activity should trigger QuackersCrazySales.Ducks?delete_error=NA2.

In this final step, the newly created pipeline should be validated and debugged.