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ARM vs Classic Deployment in Azure

When Azure Resource Manager (ARM) was created back in 2014, I (like many) thought, “What does that mean?” Up until last year, every VM I created was in Classic Deployment model. In this Azure Every Day installment, I’d like to go over the differences between the two.

A deployment model is how you’re going to issue out and stand up your Azure resources. As you’re getting your Azure resources in place, you have the choice between Classic and ARM deployment. If you’re new to Azure, you’ll rarely see Classic except for VMs. You may hear Classic being referred to (as in “Don’t do this in Classic” or “This can only be done in Classic”), but for the most part you’ll be in ARM moving forward.

The primary difference between the two deployment models is how they are managed. Once you deploy, manage or monitor these resources within Azure, if you’re in Classic you’ll be doing everything independently. You’ll be deploying each asset independently and will be managing each asset and resource independently. So, if you have a whole application set for deployment that has 10 resources, that means 10 deployments and 10 independent management components.

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And if you are going to remove it, you’re going to remove 10 different resources. ARM allows you to group the resources together as a group. They can be deployed, and looked at or managed, as a group. This is the biggest difference between ARM and Classic deployment model. Some other things to be aware of:

  • If you’re using Classic cloud server, it’s only available in Classic. The upgrade means you’re going to have to move it over.
  • If you’re using VMs, storage, or virtual networks, those can be done in either mode. (Don’t listen to people who tell you it can only be done in Classic.)
  • The biggest thing to keep in mind, every other resource or new thing within Azure is going to ARM. So, if you want the newest technology, use ARM.

DataOnWheels

Steve Hughes is a Principal Consultant at Magenic. His area of expertise is in data and business intelligence architecture on the Microsoft SQL Server platform. He was also the data architect for a SaaS company which delivered a transportation management solution for fleets across the United States. Steve has co-authored two books and delivered more than 30 presentations on SQL Server and data architecture over the past six years. He also provides insights from the field on his blog at http://dataonwheels.wordpress.com.

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