What is Infrastructure as Code?
Describing servers, networks and cloud services in versioned code files instead of configuring them by hand, so infrastructure is created and recreated reproducibly.
Infrastructure as Code means describing infrastructure in versioned code files, instead of configuring it by clicking through a cloud console or running commands by hand on a server. Tools like Terraform, OpenTofu or a cloud's native options read those files and create, modify or destroy resources accordingly: networks, virtual machines, managed databases, permissions. The file becomes the authoritative description of what the environment should be, not a reminder of what someone once did. Configuring by hand works as long as it only needs doing once: the problem arrives at the second environment, at rebuilding after an incident, or when the person who remembered the exact steps leaves the company, and without code the real infrastructure silently drifts from whatever anyone has in their head. With IaC, every change goes through a reviewable pull request like any other software change, and recreating an identical environment, for disaster recovery or a new test environment, becomes running the same code instead of repeating a procedure from memory.
Why it replaces "click and pray"
Configuring by hand works as long as it only needs doing once. The problem arrives at the second environment, at rebuilding after an incident, or when the person who remembered the exact steps leaves the company: without code, the real infrastructure silently drifts from whatever anyone has in their head. With IaC, every change goes through a reviewable pull request like any other software change, and recreating an identical environment, for disaster recovery or a new test environment, becomes running the same code instead of repeating a procedure from memory.
Why it is the prerequisite for DataOps
For a data platform, IaC is not an infrastructure-team detail: it is what makes it possible to treat different environments, development, test, production, as consistent instances of the same code instead of divergent configurations built by hand over time. It is the layer DataOps rests on: without reproducible environments, automating pipelines and tests stays a partial exercise, because there is always that one hand-configured environment nobody dares touch.
Related terms
- CI/CD · Continuous Integration and Continuous Delivery: every software change is tested and shipped to production automatically and frequently.
- DataOps · DevOps discipline applied to data pipelines: versioning, testing, CI/CD and monitoring on data flows.
- Platform engineering · The discipline building self-service internal platforms for teams: golden paths, shared tooling and infrastructure treated as a product.
- GitOps · Git as the single source of truth for what runs in production: the desired state lives in the repository, a controller applies it to the real system.
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