Datasources in Terraform – Smart & Essential Guide

Datasources in Terraform play a crucial role when you need to reference existing infrastructure that is not directly managed by your Terraform configuration. Whether you’re working with resources provisioned manually, via other tools, or in different Terraform directories, datasources allow you to bring that external information into your configuration seamlessly.

This guide will walk you through how datasources work in Terraform, why they matter, and how to implement them effectively across projects of all sizes.

What Are Datasources in Terraform?

Datasources in Terraform allow your configuration to read external resource information without managing the lifecycle (create, update, delete) of those resources.

They are especially useful when:

• You want to refer to infrastructure provisioned manually.
• You’re consuming outputs from another Terraform configuration.
• You need dynamic values from cloud services, like an AMI ID from AWS.

Unlike managed resources, which Terraform fully controls, datasources are read-only references. This allows you to reuse data without altering the original resource.

Why Use Datasources in Terraform?

There are several practical reasons to leverage datasources:

• Cross-environment integration: You may want to link different layers of your infrastructure (e.g., backend database created in one module and frontend in another).
• Access existing resources: Resources created via Ansible, CloudFormation, or manual provisioning can be read and utilized.
• Reduce duplication: Instead of hardcoding values or duplicating configuration, use datasources to dynamically fetch the latest information.

In short, datasources make Terraform more modular, flexible, and reliable.

Understanding the Datasource Block Syntax

A datasource is defined using the data block in Terraform. Its syntax closely resembles that of the resource block, making it easy for anyone familiar with Terraform to adopt.

Example Structure

data "local_file" "dog" {
  filename = "/root/dogs.txt"
}

Here’s how it breaks down:

• data: Indicates this is a data source block.
• "local_file": Specifies the type of data source. In this case, it reads content from a local file.
• "dog": A logical name used to reference the data elsewhere.

You can then reference the data using:

data.local_file.dog.content

This can be plugged into other resource definitions. For example, the content of one file can be used as the input of another.

Real-World Use Case of Datasources

Let’s say you’ve created a file called dogs.txt using a shell script. This file contains the line:
“Dogs are awesome.”

Now, you want another resource managed by Terraform—like petstore.txt—to use this file’s content. Since Terraform didn’t create dogs.txt, it cannot manage it directly. But with a datasource, you can still read it.

Implementing the Data Reference

data "local_file" "dog" {
  filename = "/root/dogs.txt"
}

resource "local_file" "petstore" {
  content  = data.local_file.dog.content
  filename = "/root/petstore.txt"
}

Terraform will read the content from dogs.txt and write it into petstore.txt during the apply phase.

Datasources vs Resources in Terraform

Understanding the difference between these two is vital:

Feature	Resources	Datasources
Purpose	Create, update, delete resources	Read existing resource data
Block keyword	resource	data
Management	Fully managed by Terraform	Read-only reference
Examples	AWS EC2, S3, local_file	AWS AMI, local_file (read), outputs from other configs

Think of resources as builders, and datasources as readers.

Supported Datasources Across Providers

Every major provider on the Terraform Registry offers various datasources. For example:

• AWS: aws_ami, aws_vpc, aws_security_group
• Azure: azurerm_resource_group, azurerm_storage_account
• GCP: google_compute_image, google_dns_managed_zone
• Kubernetes: kubernetes_namespace, kubernetes_secret

Each datasource has its required and optional arguments, as well as a set of attributes it returns. Always refer to the provider’s documentation to get the exact schema.

When and When Not to Use Datasources

Ideal Times to Use Datasources

• To fetch values from another tool’s infrastructure (e.g., Ansible-created instances).
• To use outputs from different Terraform configurations.
• To avoid hardcoded values and ensure more dynamic setups.

When to Avoid

• If the resource should be managed by Terraform (then use resource block).
• If the external resource is unreliable or frequently changes format—avoid referencing unstable sources directly.

Conclusion

Datasources in Terraform offer a powerful way to bridge the gap between managed and unmanaged infrastructure. By integrating existing resources into your configuration logic, you gain the flexibility to work across tools, teams, and environments without losing consistency.

They’re essential when scaling Terraform projects, promoting modularization, and avoiding configuration drift.

Whether you’re fetching the latest AMI ID or referencing a file created outside Terraform, datasources enable smarter infrastructure provisioning.

Frequently Asked Questions (FAQs)