A short while ago I wrote about setting up Continuous Deployment for Terraform using Bitbucket. Whilst that post is perfectly accurate from a technical standpoint it leads us in to a real minefield and really I don’t think it’s a very good idea to use Terraform in Continuous Deployment at all, rather a system of Continuous Delivery is more effective utilising manual deployments. This post is going to look at . . .
Terraform is a powerful Infrastructure as Code tool ideal for creating cloud environments and its flexible HCL syntax allows for the provisioning of complex environments from simple templates, saving countless hours. Often missed is the ability to template resources and use them in conjunction with Terraform’s workspaces feature to maintain concurrent versions of the same environment. When coupled with even a basic Continuous Deployment pipeline this combination of systems allows . . .
Previously we looked at implementing a CI/CD pipeline using both Terraform and Ansible for provisioning and Configuration Management. In this deployment we relied on an official Python Docker image to build our Ansible environment, however this required a few steps that add a few top-heavy steps that could be solved by creating our own Docker image instead. The sample code for this post is in my GitHub here. Speeding up . . .
In previous posts we looked at a basic example of creating Immutable Infrastructure via BitBucket Pipelines using Terraform as well as why we would want to use Immutable Infrastructure and what benefits it brings. However we didn’t look at how to extend the pipeline in to Configuration Management. We’re going to look at that now, leveraging Ansible within the pipeline to automatically configure the instances we create immediately after they . . .
Previously I’ve looked at Azure DevOps as a fantastic platform for deploying CI/CD pipelines, and it is, however it’s obvious inclination for Azure makes it something of an issue when trying to work on other public cloud providers, and Azure obviously isn’t the only game in town. There’s also the issue of complexity. Whilst Azure DevOps is incredibly flexible and powerful, this leads to complexity and we don’t always need . . .