Terraform vs. Manual Scripting - provisioning and maintaining modern platforms - Snowflake

Terraform vs. Manual Scripting: 

In today’s cloud-centric data world, managing complexity is less about raw technical ability and more about choosing the right tools for repeatable, scalable, and auditable processes. When it comes to provisioning and maintaining modern platforms like Snowflake, two foundational approaches stand out: Infrastructure as Code (IaC) with Terraform and manual scripting (SQL scripts or CLI commands) maintained in version control.

While both can be committed to git and automated, they represent fundamentally different paradigms—for how you model, govern, and evolve your data infrastructure. Understanding their differences isn’t just an academic exercise—it’s strategic. It shapes your ability to support agility, compliance, and operational excellence as your data estate grows in scale and sophistication.

This post explores the key differences between Terraform and manual scripts for managing Snowflake or any cloud data platform, highlighting where Terraform shines, where scripting remains essential, and how the two can even coexist in a pragmatic engineering toolkit.

Declarative vs. Imperative: Defining Your Data Cloud’s Blueprint

Manual scripting is inherently imperative: it spells out, line by line, what steps to take—creating users, granting access, modifying tables. The burden falls on engineers to ensure scripts are ordered correctly and handle edge cases (like whether an object already exists).

Terraform, in contrast, is declarative: you describe the desired end state of your infrastructure—what resources, with which settings—and the tool figures out what needs to be added, removed, or updated to reach that state. Think of it more as an architect’s blueprint rather than a builder’s checklist.

Why it matters:

·        With scripts, adding a new schema requires duplication or a flawless process to avoid conflicts.

·        In Terraform, you just update your resource definition; Terraform plans the steps and avoids recreating what’s already present.

Automated Drift Detection and Self-Healing

One of the key operational headaches in managing Snowflake is drift—when the reality of the environment starts to diverge from what’s defined in code.

·        Terraform maintains a local or remote "state file" that reflects the infrastructure’s real-world state. It can automatically detect drift (e.g., if someone manually grants a role in the UI) and either reconcile, alert, or allow you to re-apply the correct configuration.

·        Manual scripts have no notion of the current environment’s state; you must author checks yourself or risk silent inconsistencies creeping in.

Analogy:
Imagine a city where every repair is diligently logged, and city planners know exactly what’s changed and what’s out of sync—compare this to scribbled repair instructions passed between workers, where memory is the only source of truth.

Safe, Idempotent Application and Change Previews

With scripts:

·        Rerunning a script can cause havoc (e.g., recreate objects, duplicate entries, or throw errors if objects already exist).

·        There’s limited (if any) preview of what’s about to change, unless you invest in building bespoke dry-run logic.

With Terraform:

·        The “plan” phase previews all changes—additions, modifications, deletions—before anything executes. Stakeholders can review and approve before taking action.

·        Idempotence is built-in; applying the same configuration twice doesn’t cause destructive changes or duplication, because Terraform always brings reality in line with your desired state.

This approach reduces the anxiety and risk behind infrastructure changes, especially in regulated or high-uptime environments.

Collaboration, Review, and Team Productivity

Scripts can (and should!) be versioned with git, but:

·        Order of execution, dependency management, and cross-team change coordination are manual. Out-of-order merges or conflicting changes can break environments.

·        Modularizing scripts for repeated use requires substantial custom effort.

Terraform:

·        Encourages modular design—reusable modules for users, warehouses, schemas, etc.

·        Integrates naturally into DevOps workflows with pull requests, code review, and CI/CD pipelines.

·        Supports team scaling—different groups can safely provision and tear down their environments using parameterized modules with guardrails.

Reproducibility, Self-Service, and Environment Management

Provisioning consistent dev, test, and prod environments is the holy grail of modern engineering.

·        Terraform: Spinning up a new environment takes minutes—clone a module, customize parameters, apply, and you’re live. Experimentation and onboarding become frictionless.

·        Scripts: You risk environment drift and accidental deviations. Manual processes introduce variability that’s hard to audit and even harder to debug.

Self-service enablement means more teams can get what they need, faster—while still maintaining oversight and controls.

Auditability, Compliance, and Governance

·        Terraform’s state and version logs are a goldmine for auditors and compliance teams. Every infrastructure change—from who made it to exactly what changed—is tracked in human-readable code and immutable state history.

·        With scripts, only the intent and timing of script executions are typically captured; you must rely on supplementary logging and human discipline to maintain traceability.

For finance, healthcare, and other regulated industries, this shift from intention-based to state-based compliance is pivotal.

Extensibility and Future-Proofing

Manual scripts are agile for experimental, edge, or unsupported features—indeed, some new features in Snowflake still demand this approach while providers (like Terraform) catch up.

But the value of a Terraform-first strategy compounds as coverage improves:

·        When new resource types, security policies, or integrations become available, you simply update your resource definitions.

·        Teams already using Terraform can adopt new capabilities declaratively, rather than bolting on additional scripts and checks.

When Scripting Still Makes Sense

There are cases where scripting is the right answer:

·        Unsupported features: When Terraform providers lag behind Snowflake innovations.

·        Complex, one-off tasks: Data migrations, bulk operation scripts, or procedural logic unsuited to declarative modeling.

·        Preview or experimental features: Early adopters may need scripts until providers catch up.

However, even in these cases, scripts should be version-controlled, reviewed, and as idempotent as possible.

Hybrid Strategies and Organizational Evolution

Many organizations blend both approaches:

·        Terraformed core platform: Databases, roles, warehouses, foundational security.

·        Scripting for edge: One-off tasks, recent features, and manual overrides—plus a migration path to Terraform as providers evolve.

Gradually, as tool support grows, script coverage shrinks—leaving a leaner, more reliable, and more governable infrastructure landscape.

Big Picture: It’s About Risk, Scale, and Confidence

Terraform isn’t just “scripts in git”—it’s a philosophy of operational excellence. It delivers more than just automation; it brings reproducibility, reviewability, and a safety net as your data estate and its complexity grow. For regulated, cross-functional, or continuously evolving Snowflake environments, Terraform’s declarative model is a strategic multiplier.

Manual scripts are fast, flexible, and sometimes necessary, but as a foundation, they risk technical debt, drift, and missed opportunities for automation and auditability.

Conclusion

In the cloud data era, how you manage infrastructure can be as critical as the data and models you support. For long-lived, enterprise-grade Snowflake environments, the benefits of Terraform—declarative state, drift detection, team collaboration, reproducibility, and audit-friendly workflows—far outstrip what can be achieved with scripting alone.

Embrace Terraform where possible, complement with scripts when needed, and design your workflows for both today’s needs and tomorrow’s scale. That’s how data teams move from firefighting to future-proofing—and help their organizations thrive in a rapidly evolving digital world.