In the dynamic world of cloud storage, managing data lifecycle becomes a critical task for administrators. Azure Storage offers a robust solution for this through its Lifecycle Management feature. When combined with Terraform, a popular infrastructure-as-code tool, managing Azure Storage becomes not just efficient, but also scalable and automated. This post delves deep into using Terraform for Azure Storage Lifecycle Management, highlighting its benefits, use cases, and providing a step-by-step guide to implement it.

Why Administrators Use Azure Storage Lifecycle Management

Cost Efficiency

The primary allure of Lifecycle Management in Azure Storage is cost optimization. By automating the transition of data to less expensive storage tiers or deleting outdated data, organizations can significantly reduce storage costs.

Data Management

Lifecycle Management policies enable administrators to automatically handle data based on its age, type, and access frequency. This automated approach ensures that data is stored in the most appropriate tier, improving performance and accessibility.

Compliance and Governance

Adhering to data retention policies is crucial for legal and regulatory compliance. Azure Storage Lifecycle Management allows for the setting of policies that automatically comply with these regulations, reducing the risk of non-compliance.

Use Cases for Azure Storage Lifecycle Management

  1. Automated Data Tiering: Automatically move data to cooler storage tiers as it becomes less frequently accessed, optimizing cost without sacrificing data availability.

  2. Policy-based Deletion: Automatically delete data that's no longer needed, based on predefined policies, ensuring efficient storage utilization.

Setting Up Lifecycle Management with Terraform


  • An Azure account with appropriate permissions.
  • Terraform installed on your machine.
  • Basic knowledge of Terraform syntax and Azure services.

Implementing a Basic Setup

First, set up your Terraform provider for Azure and define a resource group and storage account:

provider "azurerm" {
  features {}

resource "azurerm_resource_group" "example" {
  name     = "example-resources"
  location = "East US"

resource "azurerm_storage_account" "example" {
  name                     = "examplestoracc"
  resource_group_name      =
  location                 = azurerm_resource_group.example.location
  account_tier             = "Standard"
  account_replication_type = "LRS"

Implementing Lifecycle Management Policy

Now, let's define a lifecycle management policy:

resource "azurerm_storage_management_policy" "example" {
  storage_account_id =

  rule {
    name    = "rule1"
    enabled = true

    filters {
      prefix_match = ["container1/prefix1"]
      blob_types   = ["blockBlob"]

    actions {
      base_blob {
        tier_to_cool_after_days_since_modification_greater_than    = 30
        tier_to_archive_after_days_since_modification_greater_than = 90
        delete_after_days_since_modification_greater_than          = 365
      snapshot {
        delete_after_days_since_creation_greater_than = 90

In this example, we define a rule that moves blobs to the cool storage tier after 30 days, to the archive tier after 90 days, and deletes them after 365 days. Snapshots are deleted after 90 days.

Best Practices and Tips

  • Regular Policy Reviews: Lifecycle policies should be reviewed and updated regularly to align with changing business needs and regulatory requirements.
  • Policy Testing: Test your policies in a non-production environment to understand their impact before going live.
  • Monitoring and Logging: Keep track of policy execution and impacts through Azure's monitoring and logging tools.


By leveraging Terraform with Azure's Storage Lifecycle Management, administrators can implement robust, automated data management strategies that optimize costs, ensure compliance, and efficiently manage data storage. This approach not only simplifies storage management but also aligns it with organizational policies and goals.