1. Retention and Archiving Strategies in Azure Storage


    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:

    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.