Retention and Archiving Strategies in Azure Storage

Question

Pulumi · Accepted Answer

Retention and archiving strategies in Azure Storage can be implemented using various Azure services, such as Blob Storage with lifecycle management policies. Below you will find a detailed explanation and Pulumi program in Python which demonstrates how to create an Azure Storage Account and apply Management Policies for data archiving and retention.

Firstly, we will create an Azure Storage Account using the `azure-native.storage.StorageAccount` resource. This is the place where all our blobs, files, queues, and tables will be stored.

Then, we configure a Management Policy on the storage account using the `azure-native.storage.ManagementPolicy` resource. Management Policies allow you to define rules about how your data is managed. For example, you can specify that blobs should be moved to a cooler storage tier if they haven't been accessed in 30 days, or deleted if they're over one year old.

Here's the detailed Pulumi Python program to set up an Azure Storage Account with a Management Policy for retention and archiving:

```python
import pulumi
from pulumi_azure_native import storage as azure_storage

# Create a resource group
resource_group = azure_storage.ResourceGroup('resource_group')

# Create an Azure Storage Account
account = azure_storage.StorageAccount('storageaccount',
    # Assign the Storage Account to the Resource Group 
    resource_group_name=resource_group.name,
    # Replication and Performance settings
    sku=azure_storage.SkuArgs(
        name=azure_storage.SkuName.STANDARD_LRS
    ),
    kind=azure_storage.Kind.BLOB_STORAGE,
    # Geographic location where the resource lives
    location=resource_group.location
)

# Define a Management Policy to handle archiving and retention
policy_rule = azure_storage.ManagementPolicyRuleArgs(
    name='retention_policy',
    type='Lifecycle',
    definition=azure_storage.ManagementPolicyDefinitionArgs(
        actions=azure_storage.ManagementPolicyActionArgs(
            base_blob=azure_storage.ManagementPolicyBaseBlobArgs(
                tier_to_cool=azure_storage.DateAfterModificationArgs(
                    days_after_modification_greater_than=30
                ),
                tier_to_archive=azure_storage.DateAfterModificationArgs(
                    days_after_modification_greater_than=90
                ),
                delete=azure_storage.DateAfterModificationArgs(
                    days_after_modification_greater_than=365
                ),
            ),
            snapshot=azure_storage.ManagementPolicySnapShotArgs(
                delete=azure_storage.DateAfterCreationArgs(
                    days_after_creation_greater_than=90
                ),
            )
        ),
        filters=azure_storage.ManagementPolicyFilterArgs(
            blob_types=['blockBlob']
        )
    )
)

# Apply Management Policy to the Storage Account
management_policy = azure_storage.ManagementPolicy("managementpolicy",
    resource_group_name=resource_group.name,
    account_name=account.name,
    # A rule defined above to implement our archiving and retention strategy
    policy=azure_storage.ManagementPolicySchemaArgs(
        rules=[policy_rule]
    )
)

# Export the primary endpoint of the Storage Account
primary_endpoint = pulumi.Output.all(account.primary_endpoints, account.primary_location).apply(
    lambda args: args[0].blob
)
pulumi.export('primary_storage_endpoint', primary_endpoint)
```

This program sets up a resource group and storage account, then applies a management policy with a rule that specifies the following lifecycle actions for base blobs in the storage:

- Move blobs to cool storage if they have not been modified for over 30 days.
- Move blobs to archive storage if they have not been modified for over 90 days. Archive storage is more cost-effective for data that is accessed infrequently and stored for at least 180 days with flexible latency requirements (typically set to 12 hours).
- Delete blobs if they have not been modified for over 365 days.

By exporting the primary endpoint of the storage account, we're able to identify the endpoint used to access the blobs after the program is successfully run.

This is a basic example and can be configured to be more complex based on specific needs and policies. Remember to change the names and configurations where necessary to match your requirements.