Tuesday, 21 February 2023

Bug In Azure API creating Data Factory Default AutoResolveIntegrationRuntime on Managed Virtual Network

Recently our terraform Infrastructure as code effort to create a datafactory resource with default AutoResolveIntegrationRuntime in Managed Virtual Network failed to create one.

We had managed_virtual_network_enabled flag on terraform and tried to use native azure cli too as a backup but both failed to create the default integration runtime on Managed Virtual Network and created a default public integration runtime. Only work around it was to create a Data Factory using ARM Template. I exported the ARM template and created the below Powershell to create the Data factory. I stored the Template and parameter file on a Fileshare and used it in the powershell to reuse and create a parameter file each time for different DF creation.
Copyright © 2023 Vinoth N Manoharan.The information provided in this post is provided "as is" with no implied warranties or guarantees.

10 comments:

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    1. The article discusses a deployment issue encountered while automating Azure Data Factory creation using Terraform and Azure CLI, where the default AutoResolveIntegrationRuntime was not provisioned within a Managed Virtual Network. As a workaround, the deployment was successfully automated using ARM Templates and PowerShell, enabling consistent infrastructure provisioning while maintaining reusable deployment templates for different environments. This highlights the importance of Infrastructure as Code (IaC) and deployment automation for managing enterprise cloud resources.

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  3. During a recent Terraform Infrastructure as Code implementation, we encountered an issue where creating an Azure Data Factory with the default AutoResolveIntegrationRuntime inside a Managed Virtual Network consistently failed. Despite enabling the managed_virtual_network_enabled flag in Terraform and attempting the same setup via the native Azure CLI, both methods resulted in a default public integration runtime instead. The only reliable workaround was deploying the Data Factory through an ARM template. To streamline this approach, the ARM template was exported and reused. A PowerShell script was then created to reference the template and parameter files stored on a file share, allowing dynamic parameterization for multiple Data Factory deployments.

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