Yesterday, I mapped the standard application deployment workflow. Today, I applied the same seven-step process to infrastructure code and the difference is clear: deploying infrastructure is not just riskier, it demands stricter discipline.
I implemented a real change by adding a CloudWatch CPU alarm to a webserver instance using Terraform.
The workflow began with version control, enforcing protected branches and pull request reviews. This is familiar from application development, but far more critical when changes can affect live infrastructure.
Next, I ran terraform plan locally. This is where the workflows begin to diverge. Instead of running code, Terraform generates a diff against the current state, showing exactly what will change. This step is non-negotiable because even a small misconfiguration can have a large impact.
After creating a feature branch and committing the change, I opened a pull request and included the full plan output. Unlike application code reviews, this is not just about logic. Reviewers must evaluate cost, security, and potential blast radius.
Once approved, the change was merged and tagged. Deployment was then executed using a saved plan file:
terraform apply day21.tfplan
This ensures that what was reviewed is exactly what gets applied, eliminating drift between plan and execution.
To make the workflow safe, I implemented key safeguards: plan file pinning, blast radius documentation, and approval gates for changes. I also explored Sentinel policies, which act as a guardrail by enforcing rules before any deployment can proceed.
The biggest lesson is this: infrastructure deployments are not forgiving. Application failures can often be rolled back quickly. Infrastructure mistakes can cascade across systems.
Infrastructure as Code only works at scale when it is treated with the same rigor as software engineering, plus an extra layer of caution.
Top comments (0)