Creating a new archetype_id
As we shape our environment and address our needs, imagine that the mg-example-custom we created does not yet have defined policies and is only inheriting policies from the Management Groups above....
As we shape our environment and address our needs, imagine that the mg-example-custom we created does not yet have defined policies and is only inheriting policies from the Management Groups above....
In some cases, we may have an initiative, policy, or custom role created by the module that doesn’t meet our business needs, and we would like or need to remove it. For this, we have the flexibilit...
In the previous post, we explained how the library_path parameter works and outlined the requirements for customizing policies and initiatives. Now, it’s time to put that knowledge into practice. ...
What does the library_path parameter do? The library_path parameter defines the location of a custom library folder for archetype. Why use a custom library? The module enables the creation of a ...
On January 10th, 2025, I had the pleasure of making my first appearance on Rafael Ferreira’s LowOps channel for a laid-back chat. 💬 I must admit, I’m more of a low-profile person and usually stay ...
What does the custom_landing_zones parameter do? The custom_landing_zones parameter is used to deploy additional Management Groups within the core Management Group hierarchy. Why customize Manag...
What is a Management Group? A Management Group in Azure is a hierarchical structure designed to centralize and streamline the organization and management of multiple subscriptions. Acting as a cont...
Overview The core capability of this module deploys the foundations of the conceptual architecture for Azure landing zones, with a focus on the central resource organization. Resource types ...

https://github.com/Azure/terraform-azurerm-caf-enterprise-scale is a set of Terraform modules developed by Microsoft to aid in the deployment and management of resources in Azure following the best...
Deployment type overview Azure OpenAI offers in this moment, three main deployment types, each optimized for different latency, throughput, and cost requirements: Global Standard: Configured f...