IT FinOps, also known as Financial Operations for IT, focuses on managing and optimizing the financial aspects of IT operations. The goal of IT FinOps is to maximize the value of IT investments and reduce unnecessary costs.
IT FinOps teams monitor costs, analyze usage patterns, identify inefficiencies and implement cost-saving measures. An IT FinOps team works with other disciplines such as finance, procurement, and operations to ensure that all IT spending is accounted for and optimized.
IT FinOps becomes even more important when moving to the public cloud, which can introduce complex cost structures. In a cloud environment, pay-as-you-go is the standard. However, most cloud providers also offer discounts if a certain number of resources are reserved in advance for an extended period of time. The types of charges can vary widely. For example, for a VM, you pay per second the VM is up; for a network connection, you pay for the number of bytes sent and received; and for storage, you pay for the amount of storage, the storage tier, and the number of reads and writes.
This entry was posted on Thursday 27 November 2025
There are a number of scenarios that can be used to put the new infrastructure in production as the replacement for an existing system – to “Go Live”:
- Big Bang – In the big bang scenario, at a set time, the existing system is switched off and the new system is immediately put in production, possibly after a short data migration run. This is the riskiest scenario because it may be impossible to roll back to the old system after the system is live for some time, and because downtime can occur when something goes wrong during the switchover.
- Parallel changeover – In this scenario, both the new and the existing system run simultaneously for some time (typically weeks). This allows for testing the new system on both functionality and non-functional attributes, and ensuring it works with live production data before switching off the existing system. As both systems are running and processing data, switching back is possible at any time, minimizing risk. A big disadvantage of this scenario is the cost of maintaining both systems and the possible extra work to keep both systems in sync. Also, many system designs don’t allow running two systems in parallel, for instance, if the system has many interfaces with other systems.
- Phased changeover – In a phased scenario, individual components or functionalities of the existing system are taken over by the new system, one by one. This reduces risk, as the changeover can be done gradually and controlled. This scenario can be quite costly, since typically many interfaces between the existing and the new system must be created and maintained. These new interfaces introduce new risk to the scenario, as they must be tested extensively and could fail in production. Also, the existing system must be kept online until the last component or functionality is moved to the new system, which can take considerable time and can lead to high cost.
While in theory a big bang scenario has the highest risk, in practice, it is most often used, as the scenario is the least complex to execute, and because the risk is limited to the changeover moment, when the project team is at full strength and ready to jump in if anything fails.
The go-live should be very well prepared. After the go-live scenario is determined, a step-by-step plan must be created describing each step in the scenario in detail. This plan must be reviewed, tested and improved multiple times, well in advance of the go-live date to eliminate possible surprises and to minimize risk. The scenario should include intermediate tests and multiple “go/no go” milestones, where the go-live can be aborted if anything unexpected happens. The plan should also have a defined point of no return – a go decision at this point means there is no way back to the old system. Either because there is no time left to move back to the original situation, or because an irreversible step is taken (like an update of a critical data model).
At the go-live date, high alert is needed from the project team and from the systems managers, service desk and senior management to be able to fix any issues that might arise.
After the new system is live, on-site support should be available for a predetermined time to resolve problems that may arise after the system is live; problems for which the service desk cannot yet be responsible.
This entry was posted on Thursday 06 November 2025
Where IaC defines infrastructure components, configuration management tools define the configuration of those infrastructure components. For example, a VM can be deployed using IaC, but the software that runs on that VM, or its operating system parameters, must be configured afterwards. This is a job for configuration management tools. Configuration management also uses declarative languages that define the desired state of the configuration.
The most used configuration management tools are Ansible, Puppet, and Chef.
Ansible uses YAML playbooks to define resources and can be used to automate server provisioning, configuration management, application deployment, and more. Ansible is agentless and can be used to manage a wide range of platforms, including cloud and on-premises servers.
As an example, the following Ansible playbook creates a httpd webserver on a Linux server:
- name: Install httpd
hosts: webserver
become: yes
tasks:
- name: Install httpd package
yum:
name: httpd
state: present
- name: Start httpd service
service:
name: httpd
state: started
enabled: yes
Puppet is a configuration management tool that can be used to manage servers, networks, storage, and more. The following Puppet manifest creates a httpd webserver on a Linux server:
# Install httpd package
package { 'httpd':
ensure => 'installed',
}
# Start httpd service
service { 'httpd':
ensure => 'running',
enable => true,
}
Chef is another configuration management tool. It uses a domain-specific language called Ruby to define infrastructure resources and provides features like idempotency, versioning, and testing. Chef can be used to manage servers, networks, storage, and more.
The following Chef recipe creates a httpd webserver on a Linux server:
# Install Apache HTTP Server (httpd) package
package 'httpd' do
action :install
end
# Start and enable httpd service
service 'httpd' do
action [:start, :enable]
end
Configuration management tools are often run periodically, for instance every few hours, to ensure any manual change to the deployed environment is reverted to the state as defined in the configuration management tool.
This entry was posted on Wednesday 01 October 2025
There are several commonly used IaC languages. Below are some of the most popular ones.
Terraform is a popular open-source tool and Domain-Specific Language (DSL) for building, changing, and versioning infrastructure. Terraform is cloud agnostic, which means that it has a generic syntax can be used to configure a wide range of cloud providers and infrastructure platforms, including AWS, Azure, GCP, Kubernetes, Red Hat OpenShift, databases like MySQL and PostgreSQL, firewalls, and more. But it must be noted that each platform needs its own configuration details – in Terraform, configuring an EC2 VM in AWS is done differently than configuring a VM in Azure.
As an example, the following Terraform code creates a virtual machine in Azure:
Resource "azurerm_network_interface" "mynic" {
name = "myvm1-nic"
location = "northeurope"
resource_group_name = "MyRG"
ip_configuration {
name = "ipconfig1"
subnet_id = azurerm_subnet.frontendsubnet.id
private_ip_address_allocation = "Dynamic"
}
}
Resource "azurerm_windows_virtual_machine" "example" {
name = "myvm1"
location = "northeurope"
resource_group_name = "MyRG"
network_interface_ids = [azurerm_network_interface.mynic.id]
size = "Standard_B1s"
admin_username = "adminuser"
admin_password = "Password123!"
source_image_reference {
publisher = "MicrosoftWindowsServer"
offer = "WindowsServer"
sku = "2019-Datacenter"
version = "latest"
}
os_disk {
caching = "ReadWrite"
storage_account_type = "Standard_LRS"
}
}
As a comparison, the following Terraform code creates an EC2 virtual machine in AWS:
resource "aws_instance" "example" {
ami = "ami-0be2609ba883822ec" # Windows Server 2019 Base
instance_type = "t2.micro"
key_name = "my_keypair"
vpc_security_group_ids = [aws_security_group.allow_rdp.id]
subnet_id = "subnet-12345678"
associate_public_ip_address = true
private_ip = "10.0.1.10" # Private IP address of the instance
user_data = <<-EOF
<powershell>
# Set the administrator password
net user Administrator <password>
</powershell>
EOF
}
}
As you can see, the syntax is the same, but the way the virtual machine is created is different between the cloud providers.
Azure Resource Manager (ARM) templates are JSON files that describe Azure infrastructure resources. ARM templates provide a declarative syntax for defining the infrastructure resources and their dependencies, as well as the configuration settings for each resource.
Azure Bicep is a Domain-Specific Language (DSL) for Microsoft Azure. Bicep builds on top of ARM templates and provides an abstraction layer that allows developers to write code that is easier to read and write than ARM templates. Bicep supports the same resources and functionality as ARM templates, but with a more intuitive syntax, better error handling, and reusable modules.
The following Bicep script creates a virtual machine in Azure:
param location string = 'eastus'
param vmName string = 'myVm'
param adminUsername string = 'admin'
param adminPassword string = 'password'
resource vm 'Microsoft.Compute/virtualMachines@2021-04-01' = {
name: vmName
location: location
tags: {
environment: 'dev'
}
properties: {
hardwareProfile: {
vmSize: 'Standard_D2_v3'
}
storageProfile: {
imageReference: {
publisher: 'MicrosoftWindowsServer'
offer: 'WindowsServer'
sku: '2019-Datacenter'
version: 'latest'
}
osDisk: {
createOption: 'FromImage'
}
}
osProfile: {
computerName: vmName
adminUsername: adminUsername
adminPassword: adminPassword
}
networkProfile: {
networkInterfaces: [
{
id: resourceId('Microsoft.Network/networkInterfaces', '${vmName}-nic')
}
]
}
}
}
resource nic 'Microsoft.Network/networkInterfaces@2021-02-01' = {
name: '${vmName}-nic'
location: location
properties: {
ipConfigurations: [
{
name: 'ipconfig1'
properties: {
subnet: {
id: resourceId('Microsoft.Network/virtualNetworks/subnets', 'myVnet', 'default')
}
privateIPAllocationMethod: 'Dynamic'
}
}
]
}
}
Google Cloud Deployment Manager allows to define and manage GCP cloud infrastructures using YAML or Python templates. It is similar to Azure ARM templates. Google Cloud Deployment Manager defines and manages GCP resources, such as Compute Engine virtual machines, Google Kubernetes Engine clusters, Cloud Storage buckets, and Cloud SQL databases.
The following Cloud Deployment Manager YAML script creates a virtual machine in GCP:
resources:
- name: my-vm
type: compute.v1.instance
properties:
zone: us-central1-a
machineType: zones/us-central1-a/machineTypes/n1-standard-1
disks:
- deviceName: boot
type: PERSISTENT
boot: true
autoDelete: true
initializeParams:
sourceImage: projects/debian-cloud/global/images/family/ debian-10
networkInterfaces:
- network: global/networks/default
accessConfigs:
- name: External NAT
type: ONE_TO_ONE_NAT
AWS CloudFormation allows to define and manage AWS cloud infrastructures using JSON or YAML templates. It is similar to Azure Resource Manager (ARM) templates and Google Cloud Deployment Manager. CloudFormation can define and manage AWS resources, such as EC2 instances, S3 buckets, and RDS databases.
The following CloudFormation script creates an EC2 virtual machine in AWS:
AWSTemplateFormatVersion: '2010-09-09'
Resources:
EC2Instance:
Type: 'AWS::EC2::Instance'
Properties:
ImageId: 'ami-0c55b159cbfafe1f0' # Ubuntu 20.04 LTS
InstanceType: 't2.micro'
KeyName: 'my-key-pair'
NetworkInterfaces:
- GroupSet:
- 'sg-0123456789abcdef' # security group
AssociatePublicIpAddress: 'true'
DeviceIndex: '0'
DeleteOnTermination: 'true'
This entry was posted on Wednesday 30 April 2025
The goal of edge computing is to bring computing power and data storage closer to where it is needed, rather than relying on a cloud or on-premises datacenter. In edge computing, compute and storage take place on devices at the edge of the network, such as routers, gateways, switches, and sensors.
Edge computing can be a viable option where low latency, high bandwidth, and real-time processing are critical. For example, in the case of autonomous vehicles, real-time decision making is critical for safety. In this scenario, edge computing can enable the vehicle to process data and make decisions locally, rather than sending all sensor data to a centralized datacenter.
Edge computing is also gaining popularity in Internet of Things (IoT) applications, where a large number of devices generate data that must be processed in real time. By using edge computing, organizations can reduce the amount of data that needs to be sent to the cloud, which can reduce costs and improve performance.
This entry was posted on Monday 31 March 2025
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