Vault offers an array of flexible storage backends with a view to providing a highly available storage location to store secrets, this is a great baked-in design choice as if you make Vault an integral part of your infrastructure you can ill afford a sudden outage, a perfect platform for storing structured data is, of course, a RDBMS (Relational Database Management System), as many of the mainstays are scalable and . . .
In a previous post we’ve looked at how to build Azure infrastructure with Terraform, handle sensitive secrets by storing them within Vault and centrally manage states within Azure Object Storage (confusingly called Containers). In this post we’ll take a look at the same solution but leverage the same technology within AWS, making use of AWS S3 object storage platform and using Terraform to provision further AWS resources. Sample code for . . .
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 . . .