Deploying Azure Automation Account and Runbooks via Terraform

 

Azure Automation Accounts leverage Azure Runbooks to automate processes within organizations’ Azure tenants. This process can be very powerful and help organizations effectively manage, scan, and update their environments. This post is not about Azure Automation Accounts or Azure Runbooks but rather the process by which to deploy these Accounts and their associated scripts via Terraform.

If unfamiliar, Terraform is an open-source Infrastructure as code provider. One of it’s biggest selling points is that it can be used for deploying to a plethora of providers. Since we are dealing with Azure we will be using the Azure provider. We will also assume that you are already familiar with how to deploy Terraform to Azure. If you are not here is the Terraform walkthrough for Azure.

The first step in the deployment will be creating the Azure Automation Runbook. This is done via the azurerm_automation_account resource like below:

resource "azurerm_automation_account" "aa_demo" {
  name                = 'aademo'
  location            = azurerm_resource_group.rg_automation_account.location
  resource_group_name = azurerm_resource_group.rg_automation_account.name

  sku_name = "Basic"

}

This automation account is referencing a resource group that will also be created as part of the Terraform file. Automation Accounts like any other Azure resource requires a Resource Group. The Resource Group is setup like:

resource "azurerm_resource_group" "rg_automation_account" {
  name     = 'rg-aatest-dev-eus
  location = "east us"

}

Unfortunately, the ability to create the Automation Account as a “RunAsAccount” cannot be configured at this this via Terraform. RunAsAccount is similar to a Managed Identity in Azure, aka the script will run as the resource…..thus RunAsAccount. That being said there is a github issue that outlines the steps that one could work around this. However; for initial setup it might be easier to create the Automation Account and toggle the Run. This located after the Automation Account has been created by going to “Run as Accounts”-> Create

So at this point the Terraform file will create the Resource Group and the Azure Automation Account. However, we still need to create the Runbook and upload the code that will be ran under it.

I have found the easiest way to do this is to store the script to be ran in the same project as the Terraform file. In this case we have the script in runbooks\poweshell\demo.ps1

This script will need to be imported into the Terraform file as a data reference of type local file. A block like this should do the trick:

data "local_file" "demo_ps1" {
  filename = "../runbooks/powershell/demo.ps1"
}

Once we do this Terraform is aware of the existence of the demo.ps1 file. This is important as we will pass this reference to the Runbook.

To create the Runbook we will leverage the azure_automation_runbook resource.

resource "azurerm_automation_runbook" "demo_rb" {
  name                    = "Demo-Runbook"
  location                = azurerm_resource_group.rg_automation_account.location
  resource_group_name     = azurerm_resource_group.rg_automation_account.name
  automation_account_name = azurerm_automation_account.aa_demo.name
  log_verbose             = "true"
  log_progress            = "true"
  description             = "This Run Book is a demo"
  runbook_type            = "PowerShell"
  content                 = data.local_file.demo_ps1.content
}

The content argument is key as that will pass the script that was referenced earlier and upload it’s contents as part of the deployment.

So now that the Automation Account has been created and the Runbook the demo.ps1 file can be executed in Azure. However; the need may still arise to schedule the execution of the demo.ps1 script. To do this we can leverage the azurerm_automation_job_schedule resource with first a schedule defined via azurerm_automation_schedule.

First the schedule:

resource "azurerm_automation_schedule" "sunday" {
  name                    = "EverySundayEST"
  resource_group_name     = azurerm_resource_group.rg_automation_account.name
  automation_account_name = azurerm_automation_account.aa_demo.name
  frequency               = "Week"
  interval                = 1
  timezone                = "America/New_York"
  description             = "Run every Sunday"
  week_days               = ["Sunday"]
}

This schedule is agnostic of the current Runbook and can be reused multiple times.

Next is the Terraform that links the Runbook and the schedule together:

resource "azurerm_automation_job_schedule" "demo_sched" {
  resource_group_name     = azurerm_resource_group.rg_automation_account.name
  automation_account_name = azurerm_automation_account.aa_demo.name
  schedule_name           = azurerm_automation_schedule.sunday.name
  runbook_name            = azurerm_automation_runbook.demo_rb.name
  depends_on = [azurerm_automation_schedule.sunday]
}

Now normally with Terraform the depends_on does not need to be declared as it should recognize that the sunday schedule is being reference and thus infer the demo_sched won’t run until the sunday schedule is created. However, at the time of this blog post there is an open bug on this issue. Thus, the workout is the explicitly call out the dependency.

After this everything is all done! Congratulations you should now be able to deploy an Azure Automation Account, Azure Runbooks, Schedules, and associated scripts via Terraform!

Automating Cloud Infrastructure Management for AWS Projects with Terraform

 

Automating infrastructure management helps to enhance control over a product’s environment, optimize resource use, and reduce spending on cloud infrastructure maintenance. With the right tool in place, you can describe infrastructure in code: create it once and simply copy it to new applications, making only a few changes.

In this article, we compare three tools that manage infrastructure as code: AWS Cloud Development Kit (CDK), AWS CloudFormation, and Terraform. We also show how to create and automate the management of cloud infrastructure in a way that we can later use on other projects.

This article will be useful for cloud infrastructure management and DevOps teams looking for a way to optimize their work as well as for those who want to learn how to use Terraform to automate infrastructure configuration.

Written by:

Vitalii L.,

DevOps Engineer,

System Programming Team

and

Sergei Zubkov,

QA Lead,

Software Testing Team

Contents:

Why manage infrastructure as code?

3 tools for managing AWS infrastructure as code

Configuring infrastructure for an AWS project with Terraform and Terragrunt

Conclusion

Why manage infrastructure as code?

IT infrastructure management oversees the performance of infrastructure elements needed for software to deliver business value. These elements include physical equipment like endpoints, servers, and data storage as well as virtual elements like network and app configurations, interfaces, and policies.

Usually, DevOps engineers are in charge of IT infrastructure. They need to keep it flexible, easily scalable, secure, and controllable. To achieve these goals, DevOps engineers containerize applications, deploying and managing them with tools like Docker.

Containerization allows for running an application in a manageable cluster without the need to manually configure the application and follow documentation step by step. Instead, engineers can use a Dockerfile to record changes and transfer code from one environment to another.

A containerized application can be deployed on a physical server, virtual machine, or cloud service. A cloud service is the most convenient option, since it comes with much more benefits than downsides:

Once an application is deployed in the cloud, DevOps engineers can start working on its infrastructure. Of course, they can do it manually, but that’s a bad development practice. Automating infrastructure management processes, on the other hand, helps you to experience the following benefits:

The infrastructure as code (IaC) approach allows DevOps engineers to simplify and automate the creation, management, and monitoring of software infrastructure. With IaC, DevOps engineers can describe infrastructure elements, required policies, and resources in machine-readable configuration files. These files allow engineers to streamline resource management, copy infrastructure from one project to another, and share project knowledge.

The key downside of using IaC is the risk of duplicating errors from the initial project infrastructure when reusing it. That’s why creating configuration files requires a great deal of planning and expertise working with IaC tools. And it all starts with choosing the right tool.

Related services

Cloud Infrastructure Management Services

3 tools for managing AWS infrastructure as code

In this article, we’ll talk about managing AWS-based infrastructure and some of the tools you can use for this purpose. Particularly, we’ll go over:

AWS Cloud Development Kit, or CDK, is an open-source software development platform that allows you to specify resources for cloud applications. It ensures flexible management of containerized applications. It also allows DevOps engineers to write infrastructure code in JavaScript, TypeScript, Python, C#, Java, .NET, and Go.

On the downside, AWS CDK requires perfect knowledge of programming languages to be able to configure infrastructure properly. That creates an additional challenge for DevOps engineers, who usually don’t need a deep knowledge of programming languages.

AWS CloudFormation is an infrastructure as code solution that provides you with a simple way to model AWS and third-party resources, allocate infrastructure resources within minutes, and manage them during the whole lifecycle. The key benefit of AWS CloudFormation is its support of YAML configuration files. They help to easily organize infrastructure code.

The key downsides of AWS CloudFormation are that it only supports AWS cloud services and requires learning a specific syntax.

Terraform is an open-source tool that allows you to define and submit cloud infrastructure using the HashiCorp configuration language or JSON. Both have convenient and easy-to-understand syntax.

The key benefit of implementing cloud automation using Terraform is support for all major cloud computing services: AWS, Google Cloud Platform, Microsoft Azure, and DigitalOcean. Terraform also supports the Kubernetes API. Plus, it has detailed documentation and many ready-to-use modules.

For a better experience, use Terraform with Terragrunt — a wrapper that provides you with additional tools to store infrastructure configurations and allows you to use modules.

With these advantages, Terraform appears to be the most convenient choice to automate the management of cloud infrastructure. This tool is more versatile than AWS CDK or AWS CloudFormation, as it allows you to work with various cloud services and use ready-made modules. That’s why in our own cloud infrastructure management activities, we mostly rely on Terraform.

With that in mind, let’s see how to use Terraform to automate AWS cloud infrastructure configuration and management.

Configuring infrastructure for an AWS project with Terraform and Terragrunt

Configuring project infrastructure as code allows us to upload it to the repository that we’ll later use to deploy the application. Terragrunt stores temporary files and sensitive data in the cloud so we can access them from various machines and don’t have to upload them to Git.

Here’s how Terraform can automate AWS cloud infrastructure:

But first, we need to create the elements of our environment. To do it, let’s create the following files and folders at the root of our repository:

1. The terragrunt.hcl file contains most of the Terragrunt configuration information: the region, DynamoDB table for temporary variables, and bucket for status file storage.

Status files are Terraform’s artifacts that store data on created resources. At each launch, Terraform compares the current project infrastructure with the corresponding status file, applies changes, and edits this file.

Figure 1. terragrunt.hcl contents

2. The tfvars file is for creating basic variables applicable to all environments. For example, these can be SSH administrator keys.

Figure 2. common.tfvars contents

3. The modules folder contains all the modules you use. With Terragrunt, you can use one module multiple times in different projects. You can also add third-party modules by adding a link to the corresponding repository or module branch.

Figure 3. ACM module that creates SSL certificates

4. The environments/qa folder contains HCL configurations for the QA environment. For example, with the following code, we can call the ACM module that creates project certificates:

Figure 4. Calling the ACM module

5. The environments/qa/terraform.tfvars file is for all parameters that depend on the environment.

Figure 5. The contents of the environments/qa/terraform.tfvars file

As soon as the infrastructure code is ready, we can apply it to any AWS account without the need for additional manual activities and get a consistent result:

Figure 6. Executing infrastructure code

After we get this result, we can start automating infrastructure deployments in the cloud no matter which cloud services our projects use.

Downsides of managing infrastructure with Terraform

Keep in mind that there are several limitations in managing cloud infrastructure using Terraform that we’ve discovered when using it in our projects:

• Complex configuration change management. When Terraform is actively used by several engineering teams, changing the project infrastructure with this tool may take more time than doing it manually. Any change in the configuration has to be committed to HCL files, tested, and implemented.
• Complex permission management. To be able to work with Terraform, DevOps engineers need an account with elevated access rights. It may be complicated to divide project infrastructure into several parts and configure access rights for DevOps engineers correctly and securely.
• Out-of-time deployment of product-specific features. Terraform developers often deliver features for AWS CloudFormation and other products later than you may expect.

Related services

Cloud Computing & Virtualization Development

Conclusion

Automating cloud infrastructure management can greatly reduce the amount of time and effort DevOps engineers put into configuring the infrastructure of cloud-based projects. With the right tools and approach, you can configure infrastructure once and then reuse it in other projects, making only the necessary changes.

In this article, we showed you how to automate infrastructure deployments in the cloud with Terraform. But the skills of our DevOps and cloud infrastructure management engineers go far beyond that. Feel free to reach out if you need to leverage our expertise in your project!It was originally published on https://www.apriorit.com/

It was originally published on https://www.apriorit.com/