Kuppala Sasi Pavan

Hi, my name is Sisi Pawan. I'm a DevOps and cloud engineer with over 3 years of hands-on experience in optimizing IT operations. I have a strong background in designing and maintaining Azure environments and also automating tasks with Terraform and PowerShell scripting, and have used containerization using Docker and Kubernetes. So, overall, coming to my professional experience, I'm currently working as an Azure analyst at Sopra Steria. With a main focus on UK clients like Tesco, I design and implement CICD pipelines and workflows using GitHub Actions to automate application deployment. I manage Kubernetes clusters for high availability and scalability, and I have optimized container resource allocation, reducing infrastructure cost by 15%. Coming to my self-intro conclusion, I'm very passionate about leveraging my skills in a DevOps engineer role. So I want to contribute my skills to a very dynamic team. Thanks for your time.

So we can use Python in many ways. There are many libraries in Python where we can import into Azure resources. So using those import libraries, we'll automate the deployment of Azure resources with compliance to Azure policy constraints. Using various import libraries, we'll automate the deployment of Azure resources.

So in Terraform, how will we manage secrets required for cloud resources deployment? To manage secrets for cloud resources deployment in Terraform, we use a Key Vault or a HashiCorp Key Vault provided by Terraform itself. We'll use a Key Vault to store our secrets in the Key Vault or any certificates. It will be stored in an encrypted format. We'll use Key Vault in Terraform to manage secrets required for cloud resources deployment.

So here, we'll use Terraform workspaces. So, if you want to deploy in a multi-phase deployment strategy, if you want to use this strategy, then we will use Terraform workspaces. At the time, we can declare different types of variable files in Terraform so that, when we apply Terraform, multiple resources will be created in multiple environments. Using Terraform workspaces, we can integrate these Terraform modules with Azure DevOps pipelines. So directly in the workflow itself, we can declare workspaces of these Terraform modules so that we can deploy into multiple environments.

Importance. So we have to make sure our automation script, using either PowerShell or Python, is very optimized, and it should be a time-based solution that checks the time complexity. We need to ensure importance by using our time complexity so that our automation script using PowerShell or Python executes in a particular time complexity. We have to make sure to include very small chunks of code, where we can also use cron jobs in the script or in Python, which ensures importance for automating the script.

So, initially, we can import the JSON file into Python and modify it using Python. So to change Azure policy configuration directly in the Azure portal itself, there will be an option in the automation section. In the Azure portal, there will be an automation blade. So there, we can see where we can check this automation option so we can pass this JSON file there. So we can complete whatever the resources are there in that solution. So for each resource, the JSON file will be available. So we can pass the JSON file and we can modify it using Python. So we can use the pip command in Python to modify it to JSON.

So here, the for loop, so where VMs to start here, it won't logically provide an error there in the line itself. So, while looping it, there they have provided only 1 parameter, that is VM to start, and they haven't provided computer client. They have provided only in the for loop, 1 parameter, that is VM to start, and they haven't provided computer client. So for each VM, there should be a computer client. There should be a for loop, which needs to be looped for computer client parameter also, so that then only it will start each VM separately.

Here in this Azure policy assignment, it will fail because the way of declaring this HCL programming syntax is wrong. So it will definitely fail, and we can't apply it to resolve this. We need to change the HCL form, the syntax. We need to change the syntax in Azure RM policy assignment. So there also, we need to provide double quotes there. Only single quote is provided. And here also, the way of syntax is not correct in each line. We need to change that.

So we have to use while creating Azure resources using Terraform modules. To bring up the reliability and security, we need to use RBAC, secure and other security policies. We have to assign only limited roles to users, so they'll have only limited access. We need to configure role-based access control for each resource while creating with Terraform modules to ensure reliability and security for this well-architected framework in Azure. For reliability, we have to ensure it will take a backup of each resource so it will maintain liability. And for security, we can assign or use Azure logs also. If we log that particular resource while deploying this using Terraform modules, we can show no one will have access to delete it. So it will have security also. We can use Azure logs.

So we can use AWS and Azure multi-cloud environment in Terraform, where we can optimize this using Terraform workspaces and Terraform modules also.

We'll have to use encryption to secure sensitive information used in Terraform scripts. So, if any secrets are there, we have to first encode that into base 64. And later, we can decode it. And also, we have to store the sensitive information, like your login details or any tokens, or we need to save in secrets so that we can store the secret in a keyword, so we can fetch the details from that keyword later.