AWS App Runner and Google Cloud Run are two serverless computing platforms that can help you deploy and run containerized applications without having to worry about servers. Both platforms are relatively new, but they have quickly become popular choices for developers.

What are the similarities?

Both platforms are serverless, meaning that you don’t have to provision or manage servers. The platforms will automatically scale your application up or down based on demand, so you only pay for the resources that you use. Both platforms support containerized applications. This means that you can package your application code and dependencies into a container and deploy it to the platform. Both platforms are easy to use. You can deploy your application with a few clicks or a few commands. Both platforms are scalable. They can automatically scale your application up or down based on demand, so you can handle even the most unpredictable traffic spikes.

Google Cloud Dataflow and Azure Stream Analytics are both cloud-based streaming data processing services. They offer similar features, but there are some key differences between the two platforms.

Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. It is designed to scale automatically based on the data processing needs. Dataflow also offers various security features including IAM (Identity and Access Management), encryption, and audit logging.

Azure networking and GCP networking are both comprehensive cloud networking services that offer a wide range of features and capabilities. However, there are some key differences between the two platforms.

Azure networking offers a more traditional networking model, with a focus on virtual networks (VNets), subnets, and network security groups (NSGs). VNets are isolated networks that can be used to group together resources, such as virtual machines (VMs), storage, and applications. Subnets are smaller subdivisions of a VNet, and they can be used to further isolate resources. NSGs are used to control traffic flow within and between VNets.

There are a few ways to monitor costs in Azure. One way is to use the Azure Cost Management + Billing portal. This portal provides a graphical interface that you can use to view your costs over time, track your spending against budgets, and identify areas where you can save money.

Another way to monitor costs is to use the Azure Cost Management API. This API allows you to programmatically access your cost data and integrate it with other systems. You can use the API to create custom reports, automate cost management tasks, and integrate cost data with your budgeting and forecasting processes.

Here is a Terraform file that you can use to create a Kubernetes cluster in Azure:

provider "azurerm" {
  version = "~> 3.70.0"
  subscription_id = var.azure_subscription_id
  client_id = var.azure_client_id
  client_secret = var.azure_client_secret
  tenant_id = var.azure_tenant_id
}

resource "azurerm_resource_group" "aks_cluster" {
  name = var.resource_group_name
  location = var.location
}

resource "azurerm_kubernetes_cluster" "aks_cluster" {
  name = var.aks_cluster_name
  location = azurerm_resource_group.aks_cluster.location
  resource_group_name = azurerm_resource_group.aks_cluster.name
  node_count = 3
  vm_size = "Standard_D2s_v3"
  network_profile {
    kubernetes_network_interface_id = azurerm_network_interface.aks_cluster_nic.id
  }
  default_node_pool {
    name = "default"
    node_count = 3
    vm_size = "Standard_D2s_v3"
  }
}

resource "azurerm_network_interface" "aks_cluster_nic" {
  name = var.aks_cluster_nic_name
  location = var.location
  resource_group_name = azurerm_resource_group.aks_cluster.name

  ip_configuration {
    name = "primary"
    subnet_id = azurerm_subnet.aks_cluster_subnet.id
    address_prefix = "10.0.0.0/24"
  }
}

resource "azurerm_subnet" "aks_cluster_subnet" {
  name = var.aks_cluster_subnet_name
  resource_group_name = azurerm_resource_group.aks_cluster.name
  virtual_network_name = var.virtual_network_name
  address_prefix = "10.0.0.0/24"
}

resource "azurerm_virtual_network" "aks_cluster_vnet" {
  name = var.virtual_network_name
  location = var.location
  resource_group_name = azurerm_resource_group.aks_cluster.name

  address_space = ["10.0.0.0/16"]
}

This Terraform file will create a new Azure resource group, a Kubernetes cluster, a virtual network, and a subnet. The Kubernetes cluster will have three nodes, each of which will be a Standard_D2s_v3 VM. The virtual network and subnet will be created in the same region and resource group as the Kubernetes cluster.