The Learning Curve for DevOps

AWS, Azure, DevOps
If you're attempting to implement automation in and around your deployments, you're going to find there is quite a steep learning curve for DevOps and DevOps-style implementations. Since adopting a DevOps-style release cycle does, at least in theory, speed your ability to deliver better code safely, why would it be hard? Why is there a Learning Curve for DevOps? I recently did a presentation on a simple Continuous Integration process. Here are the tools that I used in the demo: Local Git repositoryVS CodeAzure Data StudioAWS CodeCommitAWS CodePipelineAWS CodeBuildAWS RDS PostgreSQLShell commandsYAMLDockerFlyway Also, I regularly present using Azure DevOps Pipelines too. Here's the list of tools that might be in a given demo: Local Git RepositoryVS CodeAzure Data StudioAzure Git RepositoryAzure DevOps PipelineAzure DevOps AgentRedgate DeployAzure SQL DatabaseYAMLPowerShelltSQLt While there…
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Sample Azure DevOps Pipelines

I've said it before and I will repeat myself on this because it's an important concept: DevOps is about culture and communication, not tools Now, that said, to implement the automation required in DevOps, you're going to have to get into some degree of tooling. There are a whole slew of possible tools to support you: Jenkins, Team City, Octopus and more. All these tools offer excellent solutions with variations on limits, methodologies, etc. You'll need to explore them to understand which ones are best for you and your processes. I've been doing a lot of work lately in another tool, Azure DevOps. Let me show you a little of what I've done. Azure DevOps Pipelines I don't mean for this to be a complete tutorial on setting up Azure…
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